Showing papers by "State University of New York System published in 2020"

Association of Treatment With Hydroxychloroquine or Azithromycin With In-Hospital Mortality in Patients With COVID-19 in New York State.

Abstract: Importance Hydroxychloroquine, with or without azithromycin, has been considered as a possible therapeutic agent for patients with coronavirus disease 2019 (COVID-19). However, there are limited data on efficacy and associated adverse events. Objective To describe the association between use of hydroxychloroquine, with or without azithromycin, and clinical outcomes among hospital inpatients diagnosed with COVID-19. Design, Setting, and Participants Retrospective multicenter cohort study of patients from a random sample of all admitted patients with laboratory-confirmed COVID-19 in 25 hospitals, representing 88.2% of patients with COVID-19 in the New York metropolitan region. Eligible patients were admitted for at least 24 hours between March 15 and 28, 2020. Medications, preexisting conditions, clinical measures on admission, outcomes, and adverse events were abstracted from medical records. The date of final follow-up was April 24, 2020. Exposures Receipt of both hydroxychloroquine and azithromycin, hydroxychloroquine alone, azithromycin alone, or neither. Main Outcomes and Measures Primary outcome was in-hospital mortality. Secondary outcomes were cardiac arrest and abnormal electrocardiogram findings (arrhythmia or QT prolongation). Results Among 1438 hospitalized patients with a diagnosis of COVID-19 (858 [59.7%] male, median age, 63 years), those receiving hydroxychloroquine, azithromycin, or both were more likely than those not receiving either drug to have diabetes, respiratory rate >22/min, abnormal chest imaging findings, O2saturation lower than 90%, and aspartate aminotransferase greater than 40 U/L. Overall in-hospital mortality was 20.3% (95% CI, 18.2%-22.4%). The probability of death for patients receiving hydroxychloroquine + azithromycin was 189/735 (25.7% [95% CI, 22.3%-28.9%]), hydroxychloroquine alone, 54/271 (19.9% [95% CI, 15.2%-24.7%]), azithromycin alone, 21/211 (10.0% [95% CI, 5.9%-14.0%]), and neither drug, 28/221 (12.7% [95% CI, 8.3%-17.1%]). In adjusted Cox proportional hazards models, compared with patients receiving neither drug, there were no significant differences in mortality for patients receiving hydroxychloroquine + azithromycin (HR, 1.35 [95% CI, 0.76-2.40]), hydroxychloroquine alone (HR, 1.08 [95% CI, 0.63-1.85]), or azithromycin alone (HR, 0.56 [95% CI, 0.26-1.21]). In logistic models, compared with patients receiving neither drug cardiac arrest was significantly more likely in patients receiving hydroxychloroquine + azithromycin (adjusted OR, 2.13 [95% CI, 1.12-4.05]), but not hydroxychloroquine alone (adjusted OR, 1.91 [95% CI, 0.96-3.81]) or azithromycin alone (adjusted OR, 0.64 [95% CI, 0.27-1.56]), . In adjusted logistic regression models, there were no significant differences in the relative likelihood of abnormal electrocardiogram findings. Conclusions and Relevance Among patients hospitalized in metropolitan New York with COVID-19, treatment with hydroxychloroquine, azithromycin, or both, compared with neither treatment, was not significantly associated with differences in in-hospital mortality. However, the interpretation of these findings may be limited by the observational design.

Effects of COVID-19 Lockdown on Lifestyle Behaviors in Children with Obesity Living in Verona, Italy: A Longitudinal Study.

Abstract: OBJECTIVE: The aim of this study was to test the hypothesis that youths with obesity, when removed from structured school activities and confined to their homes during the coronavirus disease 2019 pandemic, will display unfavorable trends in lifestyle behaviors. METHODS: The sample included 41 children and adolescents with obesity participating in a longitudinal observational study located in Verona, Italy. Lifestyle information including diet, activity, and sleep behaviors was collected at baseline and 3 weeks into the national lockdown during which home confinement was mandatory. Changes in outcomes over the two study time points were evaluated for significance using paired t tests. RESULTS: There were no changes in reported vegetable intake; fruit intake increased (P = 0.055) during the lockdown. By contrast, potato chip, red meat, and sugary drink intakes increased significantly during the lockdown (P value range, 0.005 to < 0.001). Time spent in sports activities decreased by 2.30 (SD 4.60) h/wk (P = 0.003), and sleep time increased by 0.65 (SD 1.29) h/d (P = 0.003). Screen time increased by 4.85 (SD 2.40) h/d (P < 0.001). CONCLUSIONS: Recognizing these adverse collateral effects of the coronavirus disease 2019 pandemic lockdown is critical in avoiding depreciation of weight control efforts among youths afflicted with excess adiposity. Depending on duration, these untoward lockdown effects may have a lasting impact on a child's or adolescent's adult adiposity level.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Abstract: Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks. IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

Advanced Electrocatalysts with Single-Metal-Atom Active Sites.

Abstract: Electrocatalysts with single metal atoms as active sites have received increasing attention owing to their high atomic utilization efficiency and exotic catalytic activity and selectivity. This review aims to provide a comprehensive summary on the recent development of such single-atom electrocatalysts (SAECs) for various energy-conversion reactions. The discussion starts with an introduction of the different types of SAECs, followed by an overview of the synthetic methodologies to control the atomic dispersion of metal sites and atomically resolved characterization using state-of-the-art microscopic and spectroscopic techniques. In recognition of the extensive applications of SAECs, the electrocatalytic studies are dissected in terms of various important electrochemical reactions, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Examples of SAECs are deliberated in each case in terms of their catalytic performance, structure-property relationships, and catalytic enhancement mechanisms. A perspective is provided at the end of each section about remaining challenges and opportunities for the development of SAECs for the targeted reaction.

Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics

Abstract: AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement

Abstract: The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M-N-C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M-N-C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MNx sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M-N-C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M-N-C catalysts to MEA performance is limited. Rationally designing morphologic features of M-N-C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M-N-C catalysts in MEAs. The most considerable challenge of current M-N-C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M-N-C catalysts viable in PEMFC technologies.

SARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases

Abstract: Wastewater surveillance may represent a complementary approach to measure the presence and even prevalence of infectious diseases when the capacity for clinical testing is limited. Moreover, aggregate, population-wide data can help inform modeling efforts. We tested wastewater collected at a major urban treatment facility in Massachusetts and found the presence of SARS-CoV-2 at high titers in the period from March 18 - 25 using RT-qPCR. We then confirmed the identity of the PCR product by direct DNA sequencing. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of March 25. The reason for the discrepancy is not yet clear, however, and until further experiments are complete, these data do not necessarily indicate that clinical estimates are incorrect. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.

NWChem: Past, present, and future

Abstract: Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

Performance enhancement and degradation mechanism identification of a single-atom Co–N–C catalyst for proton exchange membrane fuel cells

Abstract: The development of catalysts free of platinum-group metals and with both a high activity and durability for the oxygen reduction reaction in proton exchange membrane fuel cells is a grand challenge. Here we report an atomically dispersed Co and N co-doped carbon (Co–N–C) catalyst with a high catalytic oxygen reduction reaction activity comparable to that of a similarly synthesized Fe–N–C catalyst but with a four-time enhanced durability. The Co–N–C catalyst achieved a current density of 0.022 A cm−2 at 0.9 ViR-free (internal resistance-compensated voltage) and peak power density of 0.64 W cm−2 in 1.0 bar H2/O2 fuel cells, higher than that of non-iron platinum-group-metal-free catalysts reported in the literature. Importantly, we identified two main degradation mechanisms for metal (M)–N–C catalysts: catalyst oxidation by radicals and active-site demetallation. The enhanced durability of Co–N–C relative to Fe–N–C is attributed to the lower activity of Co ions for Fenton reactions that produce radicals from the main oxygen reduction reaction by-product, H2O2, and the significantly enhanced resistance to demetallation of Co–N–C. Platinum-group-metal-free, non-iron catalysts are highly desirable for the oxygen reduction reaction at proton exchange membrane (PEM) fuel cell cathodes, as they avoid the detrimental Fenton reactions. Now, a cobalt and nitrogen co-doped carbon catalyst with atomically dispersed porphyrin-like CoN4C12 sites is reported with an improved activity and durability in PEM fuel cell conditions.

Current progress of Pt and Pt-based electrocatalysts used for fuel cells

Abstract: Due to the growing demand for energy and impending environmental issues, fuel cells have attracted significant attention as an alternative to conventional energy technologies. As cost is the main inhibitor of this technology, low cost catalysts with high activity and stable catalytic performance are the key to large scale application of fuel cells. The development and recent advancements of Pt and Pt-based electrocatalysts are discussed in this review. Discussion is mainly focused on the structure and composition of Pt and Pt-based electrocatalysts, which significantly affect the catalytic activities and durability of fuel cell catalysts. The electrocatalysts including Pt single metal, Pt-based alloys (including noble alloys, non-noble alloys, metal oxide alloys, and non-metal alloys), and structure-controlled alloys (nanopolyhedra, nanodendrites, and hollow and core–shell structures) were discussed. The activity, stability, and efficiency of Pt and Pt-based catalysts for both the oxygen reduction reaction (ORR) and methanol oxidation reaction (MOR) as well as the correlation between the catalytic performance, structure optimization, and composition modulation of catalysts are also discussed.

Zinc-Mediated Template Synthesis of Fe-N-C Electrocatalysts with Densely Accessible Fe-Nx Active Sites for Efficient Oxygen Reduction.

Abstract: Owing to their earth abundance, high atom utilization, and excellent activity, single iron atoms dispersed on nitrogen-doped carbons (Fe-N-C) have emerged as appealing alternatives to noble-metal platinum (Pt) for catalyzing the oxygen reduction reaction (ORR). However, the ORR activity of current Fe-N-C is seriously limited by the low density and inferior exposure of active Fe-Nx species. Here, a novel zinc-mediated template synthesis strategy is demonstrated for constructing densely exposed Fe-Nx moieties on hierarchically porous carbon (SA-Fe-NHPC). During the thermal treatment of 2,6-diaminopyridine/ZnFe/SiO2 complex, the zinc prevents the formation of iron carbide nanoparticles and the SiO2 template promotes the generation of hierarchically pores for substantially improving the accessibility of Fe-Nx moieties after subsequent leaching. As a result, the SA-Fe-NHPC electrocatalysts exhibit an unprecedentedly high ORR activity with a half-wave potential (E1/2 ) of 0.93 V in a 0.1 m KOH aqueous solution, which outperforms those for Pt/C catalyst and state-of-the-art noble metal-free electrocatalysts. As the air electrode in zinc-air batteries, the SA-Fe-NHPC demonstrates a large peak power density of 266.4 mW cm-2 and superior long-term stability. Therefore, the developed zinc-mediated template synthesis strategy for boosting the density and accessibility of Fe-Nx species paves a new avenue toward high-performance ORR electrocatalysts.

Long-Term Results of NRG Oncology RTOG 0617: Standard- Versus High-Dose Chemoradiotherapy With or Without Cetuximab for Unresectable Stage III Non-Small-Cell Lung Cancer.

Abstract: PURPOSERTOG 0617 compared standard-dose (SD; 60 Gy) versus high-dose (HD; 74 Gy) radiation with concurrent chemotherapy and determined the efficacy of cetuximab for stage III non–small-cell lung ca...

Engineering Local Coordination Environments of Atomically Dispersed and Heteroatom‐Coordinated Single Metal Site Electrocatalysts for Clean Energy‐Conversion

Abstract: DOI: 10.1002/ ((please add manuscript number)) Article type: Review Engineering Local Coordination Environments of Atomically Dispersed and Heteroatomcoordinated Single Metal Site Electrocatalysts for Clean Energy-Conversion Yuanzhi Zhu, Joshua Sokolowski, Xiancheng Song, Yanghua He, Yi Mei*, and Gang Wu* Dr. Y. Zhu, X Song, Prof. Y. Mei Faculty of Chemical Engineering, Yunnan Provincial Key Laboratory of Energy Saving in Phosphorus Chemical Engineering and New Phosphorus Materials, The Higher Educational Key Laboratory for Phosphorus Chemical Engineering of Yunnan Province, Kunming University of Science and Technology, Kunming 650500, China. E-mail: meiyi412@126.com; J. Sokolowski, Y. He, Prof. G. Wu Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo 14260, New York, United States Email: gangwu@buffalo.edu

Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues.

Abstract: Multiple sclerosis is a chronic, demyelinating disease of the CNS. Cognitive impairment is a sometimes neglected, yet common, sign and symptom with a profound effect on instrumental activities of daily living. The prevalence of cognitive impairment in multiple sclerosis varies across the lifespan and might be difficult to distinguish from other causes in older age. MRI studies show that widespread changes to brain networks contribute to cognitive dysfunction, and grey matter atrophy is an early sign of potential future cognitive decline. Neuropsychological research suggests that cognitive processing speed and episodic memory are the most frequently affected cognitive domains. Narrowing evaluation to these core areas permits brief, routine assessment in the clinical setting. Owing to its brevity, reliability, and sensitivity, the Symbol Digit Modalities Test, or its computer-based analogues, can be used to monitor episodes of acute disease activity. The Symbol Digit Modalities Test can also be used in clinical trials, and data increasingly show that cognitive processing speed and memory are amenable to cognitive training interventions.

Single Cobalt Sites Dispersed in Hierarchically Porous Nanofiber Networks for Durable and High-Power PGM-Free Cathodes in Fuel Cells.

Abstract: Increasing catalytic activity and durability of atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts for the oxygen reduction reaction (ORR) cathode in proton-exchange-membrane fuel cells remains a grand challenge. Here, a high-power and durable Co-N-C nanofiber catalyst synthesized through electrospinning cobalt-doped zeolitic imidazolate frameworks into selected polyacrylonitrile and poly(vinylpyrrolidone) polymers is reported. The distinct porous fibrous morphology and hierarchical structures play a vital role in boosting electrode performance by exposing more accessible active sites, providing facile electron conductivity, and facilitating the mass transport of reactant. The enhanced intrinsic activity is attributed to the extra graphitic N dopants surrounding the CoN4 moieties. The highly graphitized carbon matrix in the catalyst is beneficial for enhancing the carbon corrosion resistance, thereby promoting catalyst stability. The unique nanoscale X-ray computed tomography verifies the well-distributed ionomer coverage throughout the fibrous carbon network in the catalyst. The membrane electrode assembly achieves a power density of 0.40 W cm-2 in a practical H2 /air cell (1.0 bar) and demonstrates significantly enhanced durability under accelerated stability tests. The combination of the intrinsic activity and stability of single Co sites, along with unique catalyst architecture, provide new insight into designing efficient PGM-free electrodes with improved performance and durability.

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Abstract: Machine learning-based solutions have been successfully employed for the automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features ( e.g. , requested permissions, API calls, etc. ), and the perturbations can only be implemented by simply modifying application’s manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK’s Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.

Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

Abstract: With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detection (CD). Multisource multitemporal images can provide abundant information on observed landscapes with various physical and material views, and it is exigent to develop efficient techniques to utilize these multisource data for CD. In this article, we propose a novel and general deep siamese convolutional multiple-layers recurrent neural network (RNN) (SiamCRNN) for CD in multitemporal VHR images. Superior to most VHR image CD methods, SiamCRNN can be used for both homogeneous and heterogeneous images. Integrating the merits of both convolutional neural network (CNN) and RNN, SiamCRNN consists of three subnetworks: deep siamese convolutional neural network (DSCNN), multiple-layers RNN (MRNN), and fully connected (FC) layers. The DSCNN has a flexible structure for multisource image and is able to extract spatial–spectral features from homogeneous or heterogeneous VHR image patches. The MRNN stacked by long-short term memory (LSTM) units is responsible for mapping the spatial–spectral features extracted by DSCNN into a new latent feature space and mining the change information between them. In addition, FC, the last part of SiamCRNN, is adopted to predict change probability. The experimental results in two homogeneous data sets and one challenging heterogeneous VHR images data set demonstrate that the promising performances of the proposed network outperform several state-of-the-art approaches.

Recent epidemiologic trends in periodontitis in the USA.

Abstract: The most important development in the epidemiology of periodontitis in the USA during the last decade is the result of improvements in survey methodologies and statistical modeling of periodontitis in adults. Most of these advancements have occurred as the direct outcome of work by the joint initiative known as the Periodontal Disease Surveillance Project by the Centers for Disease Control and Prevention and the American Academy of Periodontology that was established in 2006. This report summarizes some of the key findings of this important initiative and its impact on our knowledge of the epidemiology of periodontitis in US adults. This initiative first suggested new periodontitis case definitions for surveillance in 2007 and revised them slightly in 2012. This classification is now regarded as the global standard for periodontitis surveillance and is used worldwide. First, application of such a standard in reporting finally enables results from different researchers in different countries to be meaningfully compared. Second, this initiative tackled the concern that prior national surveys, which used partial-mouth periodontal examination protocols, grossly underestimated the prevalence of periodontitis of potentially more than 50%. Consequently, because previous national surveys significantly underestimated the true prevalence of periodontitis, it is not possible to extrapolate any trend in periodontitis prevalence in the USA over time. Any difference calculated may not represent any actual change in periodontitis prevalence, but rather is a consequence of using different periodontal examination protocols. Finally, the initiative addressed the gap in the need for state and local data on periodontitis prevalence. Through the direct efforts of the Centers for Disease Control and Prevention and the American Academy of Periodontology initiative, full-mouth periodontal probing at six sites around all nonthird molar teeth was included in the 6 years of National Health and Nutrition Examination Surveys from 2009-2014, yielding complete data for 10 683 dentate community-dwelling US adults aged 30 to 79 years. Applying the 2012 periodontitis case definitions to the 2009-2014 National Health and Nutrition Examination Surveys data, the periodontitis prevalence turned out to be much greater than previously estimated, namely affecting 42.2% of the population with 7.8% of people experiencing severe periodontitis. It was also discovered that only the moderate type of periodontitis is driving the increase in periodontitis prevalence with age, not the mild or the severe types whose prevalence do not increase consistently with age, but remain ~ 10%-15% in all age groups of 40 years and older. The greatest risk for having periodontitis of any type was seen in older people, in males, in minority race/ethnic groups, in poorer and less educated groups, and especially in cigarette smokers. The Centers for Disease Control and Prevention and the American Academy of Periodontology initiative reported, for the first time, the periodontitis prevalence estimated at both local and state levels, in addition to the national level. Also, this initiative developed and validated in field studies a set of eight items for self-reported periodontitis for use in direct survey estimates of periodontitis prevalence in existing state-based surveys. These items were also included in the 2009-2014 National Health and Nutrition Examination Surveys for validation against clinically determined cases of periodontitis. Another novel result of this initiative is that, for the first time, the geographic distribution of practicing periodontists in relation to the geographic distribution of people with severe periodontitis is illustrated. In summary, the precise periodontitis prevalence and distribution among subgroups in the dentate US noninstitutionalized population aged 30-79 years is better understood because of application of valid periodontitis case definitions to full-mouth periodontal examination, in combination with reliable information on demographic and health-related measures. We now can monitor the trend of periodontitis prevalence over time as well as guide public health preventive and intervention initiatives for the betterment of the health of the adult US population.

Immune checkpoint inhibitor–related dermatologic adverse events

Abstract: Immune checkpoint inhibitors have emerged as a pillar in the management of advanced malignancies. However, nonspecific immune activation may lead to immune-related adverse events, wherein the skin and its appendages are the most frequent targets. Cutaneous immune-related adverse events include a diverse group of inflammatory reactions, with maculopapular rash, pruritus, psoriasiform and lichenoid eruptions being the most prevalent subtypes. Cutaneous immune-related adverse events occur early, with maculopapular rash presenting within the first 6 weeks after the initial immune checkpoint inhibitor dose. Management involves the use of topical corticosteroids for mild to moderate (grades 1-2) rash, addition of systemic corticosteroids for severe (grade 3) rash, and discontinuation of immunotherapy with grade 4 rash. Bullous pemphigoid eruptions, vitiligo-like skin hypopigmentation/depigmentation, and psoriasiform rash are more often attributed to programmed cell death-1/programmed cell death ligand-1 inhibitors. The treatment of bullous pemphigoid eruptions is similar to the treatment of maculopapular rash and lichenoid eruptions, with the addition of rituximab in grade 3-4 rash. Skin hypopigmentation/depigmentation does not require specific dermatologic treatment aside from photoprotective measures. In addition to topical corticosteroids, psoriasiform rash may be managed with vitamin D3 analogues, narrowband ultraviolet B light phototherapy, retinoids, or immunomodulatory biologic agents. Stevens–Johnson syndrome and other severe cutaneous immune-related adverse events, although rare, have also been associated with checkpoint blockade and require inpatient care as well as urgent dermatology consultation.

Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach.

Abstract: Background: In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective: The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods: We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results: After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. Conclusions: Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

The fate of carbon in a mature forest under carbon dioxide enrichment

Abstract: Atmospheric carbon dioxide enrichment (eCO2) can enhance plant carbon uptake and growth1–5, thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO2 concentration6. Although evidence gathered from young aggrading forests has generally indicated a strong CO2 fertilization effect on biomass growth3–5, it is unclear whether mature forests respond to eCO2 in a similar way. In mature trees and forest stands7–10, photosynthetic uptake has been found to increase under eCO2 without any apparent accompanying growth response, leaving the fate of additional carbon fixed under eCO2 unclear4,5,7–11. Here using data from the first ecosystem-scale Free-Air CO2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responded to four years of eCO2 exposure. We show that, although the eCO2 treatment of +150 parts per million (+38 per cent) above ambient levels induced a 12 per cent (+247 grams of carbon per square metre per year) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone accounting for half of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO2, and challenge the efficacy of climate mitigation strategies that rely on ubiquitous CO2 fertilization as a driver of increased carbon sinks in global forests. Carbon dioxide enrichment of a mature forest resulted in the emission of the excess carbon back into the atmosphere via enhanced ecosystem respiration, suggesting that mature forests may be limited in their capacity to mitigate climate change.

The role of the stratosphere in subseasonal to seasonal prediction Part II: Predictability arising from stratosphere ‐ troposphere coupling

Abstract: The stratosphere can have a signi_cant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and tropo sphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Prob abilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Paci_c and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Nin~o Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the USA, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.