bluuebunny/crossref_metadata_2025 huggingface dataset:
Endoscopic Approach for Insertion of Custom-Designed Facial Implants
Authors: Gonzalez Luis Vicente , Cote Adriana , López Juan Pablo , Gómez-Delgado Andrés
Published: December 2020

The objective of this article is to describe the combination of virtual planning used to produce a custom-made facial implant, along with the minimally invasive endoscopic approach used to place it, for the reconstruction of the frontal bone. This management reduces the sequelae that occur with the traditional incisions while restoring the proper contour of facial surfaces at the same time.
The Effect of Autogenous Bone Graft Mixed With Recombinant Human Vascular Endothelial Growth Factor on Bone Regeneration
Authors: Tasdemir Ufuk , Iyilikçi Berrin , Aktürk Melisa Cansu , Ozmen Ozlem , Kizildağ Alper , Elmali Zuhal
Published: December 2020

Introduction:

Bone regeneration depends on vascularization in the pertaining site. This study aims to investigate autogenous bone grafts mixed with recombinant human vascular endothelial growth factor (rhVEGF) effect on bone regeneration in rat mandibular bone defect.

Material Methods:

Using 32 Wistar Albino rats, our experimental study consists of 4 groups: Group1 (control group), the defect was empty; Group 2, autogenous bone graft only; Group 3, gelatin sponge plus rhVEGF applications; Group 4, autogenous bone graft plus rhVEGF applications. The rats were sacrificed on the 28th day after the operation. New bone regeneration was analyzed histologically and immunohistochemically.

Results:

Our histological analyses revealed that new bone regeneration in Group 3 was enhanced in comparison to Group 1 and Group 2. However, autogenous bone grafts combined with rhVEGF provided the best outcome in conjunction with the increased remodeling of the new bone.

Conclusions:

In the light of our results, it can be concluded that autogenous bone grafts in combination with rhVEGF can, potentially, enhance neovascularization and bone regeneration.
The Potential Value of Mixed Reality in Neurosurgery
Authors: Zhang Chaochao , Gao Haijun , Liu Ziqiang , Huang Haiyan
Published: December 2020

Mixed reality (MR) merges virtual information into the real world through computer technology, in which the real environment and virtual objects can get spliced in the same image or space at real time so that it can effectively express and integrate the virtual and real worlds and allow high feedback interaction. This technology combines the many advantages of virtual realityand augmented reality, and has a promising future in the medical field. At present, MR technology is just at the beginning stage in the medical field in the world, whose application in neurosurgery is also rarely reported. Given this, the authors described the research progress of MR in neurosurgery including preoperative planning and intraoperative guidance, doctor-patient communication, teaching rounds, physician training, and so on.
'Candidatus Phytoplasma sacchari’, a novel taxon - associated with Sugarcane Grassy Shoot (SCGS) disease
Authors: Kirdat Kiran , Tiwarekar Bhavesh , Thorat Vipool , Sathe Shivaji , Shouche Yogesh , Yadav Amit
Published: December 2020

Sugarcane Grassy Shoot (SCGS) disease is known to be related to Rice Yellow Dwarf (RYD) phytoplasmas (16SrXI-B group) which are found predominantly in sugarcane growing areas of the Indian subcontinent and South-East Asia. The 16S rRNA gene sequences of SCGS phytoplasma strains belonging to the 16SrXI-B group share 98.07 % similarity with ‘Ca. Phytoplasma cynodontis’ strain BGWL-C1 followed by 97.65 % similarity with ‘Ca. P. oryzae’ strain RYD-J. Being placed distinctly away from both the phylogenetically related species, the taxonomic identity of SCGS phytoplasma is unclear and confusing. We attempted to resolve the phylogenetic positions of SCGS phytoplasma based on the phylogenetic analysis of 16S rRNA gene (>1500 bp), nine housekeeping genes (>3500 aa), core genome phylogeny (>10 000 aa) and OGRI values. The draft genome sequences of SCGS phytoplasma (strain SCGS) and Bermuda Grass White leaf (BGWL) phytoplasma (strain LW01), closely related to ‘Ca. P. cynodontis’, were obtained. The SCGS genome was comprised of 29 scaffolds corresponding to 505 173 bp while LW01 assembly contained 21 scaffolds corresponding to 483 935 bp with the fold coverages over 330× and completeness over 90 % for both the genomes. The G+C content of SCGS was 19.86 % while that of LW01 was 20.46 %. The orthoANI values for the strain SCGS against strains LW01 was 79.42 %, and dDDH values were 22. Overall analysis reveals that SCGS phytoplasma forms a distant clade in RYD group of phytoplasmas. Based on phylogenetic analyses and OGRI values obtained from the genome sequences, a novel taxon ‘Candidatus Phytoplasma sacchari’ is proposed.
COVID-19 Trends in Florida – August 10 – November 14, 2020
Authors: Jones Rebekah D. , Romer Jacob E.
Published: December 2020

Limited research on the prevalence and characteristics of SARS-CoV-2 (COVID-19) in K-12 environments has led to a flurry of speculative, qualitative, non-data-driven analysis with potentially dangerous implications for public health policy. Twenty-four US states provide, at a minimum, outbreak data in K-12 schools. Student enrollment across Florida’s 67 districts totals more than 2.67 million, with five of the ten most populated districts in the country in the state. This study uses Florida as a case study in COVID-19 trends in schools. With no state-wide mask mandate, varying levels of virtual-instruction participation, and a range of rural, suburban, and urban environments, Florida represents the wide range of learning environments and COVID-19 approaches to mitigation observed across the country. The Covid Monitor began tracking case data in K-12 schools in July 2020, and has since reported more than 200,000 confirmed cases of the virus in the K-12 environment nationwide – the largest date repository for cases in schools. The Covid Monitor’s national database allows for analysis of the characteristics and trends of cases in schools over time. The results may inform decisions about continued in-person and virtual learning access and promotion across the United States, as well as mitigation measures related to reopening policies across districts responsive to model results. These results also provide a baseline for monitoring trends and evaluating mitigation strategies.

Table of Contents Summary

Using the most comprehensive database of K-12 COVID-19 case data in the country, Florida provides clues for understanding student and staff cases in schools.

What’s known on this subject

Florida schools began reopening to in-person instruction in August and reported 15,393 student and staff cases of COVID-19 as of November 14, 2020. Incidence of COVID-19 cases in K-12 students and staff is of urgent public health concern.

What this study adds

COVID-19 cases reported in Florida schools were most influenced by community case rates and percent of students attending face-to-face. Student case rates were highest in high schools (4.5 per 1,000); staff case rates were highest in elementary levels (4.5-4.8).
Flexible multiplane structured illumination microscope with a four-camera detector
Authors: Johnson Karl A. , Noble Daniel , Machado Rosa , Hagen Guy M.
Published: December 2020

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the rapid acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.
Chromosome 8 gain is associated with high-grade transformation in MPNST
Authors: Dehner Carina , Moon Chang In , Zhou Zhaohe , Zhang Xiyuan , Miller Chris , Xu Hua , Wan Xiaodan , Yang Kuangying , Mashl Jay , Gosline Sara J.C. , Wang Yuxi , Zhang Xiaochun , Godec Abigail , Jones Paul A. , Dahiya Sonika , Bhatia Himanshi , Primeau Tina , Li Shunqiang , Pollard Kai , Ding Li , Pratilas Christine A. , Shern Jack F. , Hirbe Angela C.
Published: December 2020

One of the most common malignancies affecting adults with Neurofibromatosis type 1 (NF1) is the malignant peripheral nerve sheath tumor (MPNST), an aggressive and often fatal sarcoma which commonly arises from benign plexiform neurofibromas. Despite advances in our understanding of MPNST pathobiology, there are few effective therapeutic options, and no investigational agents have proven success in clinical trials. To further understand the genomic heterogeneity of MPNST, and to generate a preclinical platform that encompasses this heterogeneity, we developed a collection of NF1-MPNST patient derived xenografts (PDX). These PDX were compared to the primary tumors from which they were derived using copy number analysis, whole exome and RNA sequencing. We identified chromosome 8 gain as a recurrent genomic event in MPNST and validated its occurrence by FISH in the PDX and parental tumors, in a validation cohort, and by single cell sequencing in the PDX. Finally, we show that chromosome 8 gain is associated with inferior overall survival in soft tissue sarcomas. Taken together, these data suggest that chromosome 8 gain is a critical event in MPNST pathogenesis, and may account for the aggressive nature and poor outcomes in this sarcoma subtype.
Dynamical Indicators of Resilience from Physiological Time Series in Geriatric Inpatients: Lessons Learned
Authors: Rector Jerrald L. , Gijzel Sanne M. W. , van de Leemput Ingrid A. , van Meulen Fokke B. , Olde Rikkert Marcel G. M. , Melis René J. F.
Published: December 2020

Background

The concept of physical resilience may help geriatric medicine objectively assess patients’ ability to ‘bounce back’ from future health challenges. Indicators hypothesized to forecast resilience after a stressor have been developed under two paradigms with different perspectives: Critical Slowing Down (CSD) and Loss of Complexity (LoC). This study explored if and how these indicators, based on fluctuations in physiologic signals, can validly reflect the physical resilience of geriatric inpatients.

Methods

Geriatric patients (n = 121, 60% female) had their heart rate and physical activity continuously monitored using a chest-worn sensor. Measures of health functioning (multimorbidity, frailty and Activities of Daily Living [ADL]) were obtained by questionnaire at admission. Indicators from both paradigms (CSD: variance, autocorrelation, cross-correlation; LoC: [multivariate] multiscale entropy) were extracted from both physiological signals. The relationships among indicators and their associations with health functioning were assessed by correlation and linear regression analyses, respectively.

Results

Greater complexity and higher variance in physical activity were associated with lower frailty (β = –0.28, p=.004 and β = –0.37, p<.001, respectively) and better ADL function (β = 0.23, p=.022 and β = 0.38, p<.001). The associations of physical activity variance with health functioning was not in the expected direction based on the Critical Slowing Down paradigm.

Conclusions

Associations between dynamical resilience indicators tested here and measures of health functioning were not all in the expected direction. In retrospect, these observations stress the importance of matching the underlying assumptions of the resilience paradigm to the homeostatic role of the variable monitored.
SARS-CoV-2 infection and COVID-19 severity in individuals with prior seasonal coronavirus infection
Authors: Gombar Saurabh , Bergquist Timothy , Pejaver Vikas , Hammarlund Noah E. , Murugesan Kanagavel , Mooney Sean , Shah Nigam , Pinsky Benjamin A. , Banaei Niaz
Published: December 2020

A sizable fraction of healthy blood donors have cross-reactive T cells to SARS-CoV-2 peptides due to prior infection with seasonal coronavirus. Understanding the role of cross-reactive T cells in immunity to SARS-CoV-2 has implications for managing the COVID-19 pandemic. We show that individuals with documented history of seasonal coronavirus have a similar SARS-CoV-2 infection rate and COVID-19 severity as those with no prior history of seasonal coronavirus. Our findings suggest prior infection with seasonal coronavirus does not provide immunity to subsequent infection with SARS-CoV-2.
Test-trace-isolate-quarantine (TTIQ) intervention strategies after symptomatic COVID-19 case identification
Authors: Ashcroft Peter , Lehtinen Sonja , Bonhoeffer Sebastian
Published: December 2020

The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios.

1

Author summary

Detecting symptomatically-infected individuals and isolating them from the community is used slow the spread of an infectious disease. Additional contact tracing and quarantine can further interrupt chains of disease transmission. These measures are employed globally to control the ongoing COVID-19 pandemic. Here we use a mathematical model to quantify how effective the test-trace-isolate-quarantine (TTIQ) intervention can be against SARS-CoV-2 spread, and how delays and inaccuracies in these processes can reduce this effectiveness. With this framework we seek to improve and optimise the TTIQ intervention and to understand the problems that we could face with new variants and/or vaccine escape. We show that increasing the detection of new infections, and doing so with minimal delay after symptom onset, is key to an effective intervention.
A Comprehensive Epithelial Tubo-Ovarian Cancer Risk Prediction Model Incorporating Genetic and Epidemiological Risk Factors
Authors: Lee Andrew , Yang Xin , Tyrer Jonathan , Gentry-Maharaj Aleksandra , Ryan Andy , Mavaddat Nasim , Cunningham Alex P. , Carver Tim , Archer Stephanie , Leslie Goska , Kalsi Jatinderpal , Gaba Faiza , Manchanda Ranjit , Gayther Simon A. , Ramus Susan J. , Walter Fiona M. , Tischkowitz Marc , Jacobs Ian , Menon Usha , Easton Douglas F. , Pharoah Paul P.D. , Antoniou Antonis C.
Published: December 2020

Background

Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify females at higher risk who could benefit from targeted screening and prevention.

Methods

We developed a multifactorial EOC risk model for females of European ancestry incorporating the effects of pathogenic variants (PVs) inBRCA1, BRCA2, RAD51C, RAD51DandBRIP1, a polygenic risk score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.

Results

Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the 1stto 99thpercentiles of the EOC risk-distribution. The corresponding range for females with an affected first-degree relative is 1.9% to 10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well-calibrated in quintiles of predicted risk.

Conclusion

This multifactorial risk model can facilitate stratification, in particular among females with FH of cancer and/or moderate- and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).
Mismatch: A comparative study of vitamin D status in British-Bangladeshi migrants
Authors: Smith Nicholas , Sievert Lynnette Leidy , Muttukrishna Shanthi , Begum Khurshida , Murphy Lorna , Sharmeen Taniya , Gunu Richard , Chowdhury Osul , Bentley Gillian R
Published: December 2020

Background and objectives

Low levels of vitamin D among dark-skinned migrants to northern latitudes and increased risks for associated pathologies illustrate an evolutionary mismatch between an environment of high ultraviolet (UV) radiation to which such migrants are adapted and the low-UV environment to which they migrate. Recently, low levels of vitamin D have also been associated with higher risks for contracting COVID-19. South Asians in the UK have higher risk for low vitamin D levels. In this study, we assessed vitamin D status of British-Bangladeshi migrants compared to white British residents and Bangladeshis still living in Bangladesh (‘sedentees’).

Methodology

The cross-sectional study compared vitamin D levels among 149 women aged 35-59, comprising British-Bangladeshi migrants (n=50), white UK neighbors (n=54) and Bangladeshi sedentees (n=45). Analyses comprised multivariate models to assess serum levels of 25-hydroxyvitamin D (25(OH)D), and associations with anthropometric, lifestyle, health and migration factors.

Results

Vitamin D levels in Bangladeshi migrants were very low: mean 25(OH)D = 32.2nmol/L ± 13.0, with 29% of migrants classified as deficient (<25nmol/L) and 94% deficient or insufficient (≤50nmol/L). Mean levels of vitamin D were significantly lower among British-Bangladeshis compared to Bangladeshi sedentees (50.9nmol/L ± 13.3), presumably due to less exposure to sunlight following migration; levels were also lower than in white British women (55.3nmol/L ± 20.9). Lower levels of vitamin D were associated with increased body mass index and low iron status.

Conclusions and implications

Recommending supplements to Bangladeshi migrants could prevent potentially adverse health outcomes associated with vitamin D deficiency.

Lay summary

Vitamin D deficiency is one example of mismatch between an evolved trait and novel environments. Here we compare vitamin D status of dark-skinned British-Bangladeshi migrants in the UK to Bangladeshis in Bangladesh and white British individuals. Migrants had lower levels of vitamin D and are at risk for associated pathologies.
Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19
Authors: Davis Connor , Gao Michael , Nichols Marshall , Henao Ricardo
Published: December 2020

Using structured elements from Electronic Health Records (EHR), we seek to:i) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, andii) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.
Development of a Soluble KIT (sKIT) Electrochemical Aptasensor For Cancer Theranostics
Authors: Chung Saeromi , Sicklick Jason K. , Ray Partha , Hall Drew A.
Published: December 2020

A conformational changing aptamer based electrochemical sensor was developed for the detection of soluble KIT, a cancer biomarker, in human serum. The sensor was fabricated with a ferrocene labeled aptamer (Kd< 5 nM) conjugated to a gold electrode. Quantitative KIT detection was performed using electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV). The experimental parameters such as the ratio of aptamer to spacer, aptamer immobilization time, pH, and KIT incubation time were optimized by EIS, and the sensing surface was characterized with voltammetry. The assay specificity was investigated using interfering species showing high specificity towards the target protein. The aptasensor exhibited a wide dynamic range from 10 pg/mL to 100 ng/mL in buffer with a detection limit of 1.15 pg/mL. The sensor also exhibited a linear response with increased KIT concentrations spiked in human serum. The applicability of the sensor was successfully demonstrated using cancer cell conditioned media. The proposed aptasensor can be used in continuous or intermittent approach for cancer therapy monitoring and diagnostics (theranostics).
Community Perceptions of Vaccination Among Influential Stakeholders: Qualitative Research in Rural India
Authors: Dhaliwal Baldeep K. , Chandrashekhar Riti , Rattani Ananya , Seth Rajeev , Closser Svea , Jain Anika , Bloom David E. , Shet Anita
Published: December 2020

Background

In India and other low- and middle-income countries, multiple family and community members are influential in caregivers’ perceptions of vaccination. Existing literature indicates the primary caregiver, typically the mother, is instrumental in vaccine decision-making, but this may vary in contexts. We investigated the role of stakeholders in India who influence caregivers’ vaccination perceptions, as this is essential to developing strategies to promote vaccine acceptance and improve uptake.

Methods

This research was conducted in 2019 in Mewat District in Haryana, an area in India with extremely low vaccination coverage. We conducted six focus group discussions with 60 participants in the following categories: fathers of children under-5 years old, expectant mothers, mothers-in-law, community health workers, and community influencers such as locally elected officials and religious leaders.

Results

Our results highlighted four themes that influence vaccine uptake. First, while caregivers associated vaccination with reductions in specific diseases, they also noted that vaccination services brought broad health gains, including improved nutrition, antenatal guidance, and social support. Second, community health workers critically influenced, positively or negatively, caregivers’ vaccination perceptions. Third, community health workers faced gaps in their education such as limited training on vaccine side-effects, placing them at a disadvantage when dealing with families. Finally, we found that mothers-in-law, fathers, and religious leaders influence caregivers’ perceptions of vaccination.

Conclusions

Communication of broader benefits of vaccines and vaccination services by community health workers could be impactful in increasing vaccine acceptance. Vaccine uptake could potentially be improved by facilitating community health workers’ ownership over vaccine acceptance and uptake by involving them in the design and implementation of interventions to target mothers and mothers-in-law. A ‘bottom-up’ approach, leveraging community health workers’ knowledge to design interventions, and giving a voice to key members of the household and society beyond mothers alone, may sustain health improvement in low vaccine coverage areas.
fullsibQTL: an R package for QTL mapping in biparental populations of outcrossing species
Authors: Gazaffi Rodrigo , Amadeu Rodrigo R. , Mollinari Marcelo , Rosa João R. B. F. , Taniguti Cristiane H. , Margarido Gabriel R. A. , Garcia Antonio A. F.
Published: December 2020

Accurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developedfullsibQTL, a novel and freely availableRpackage to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations.fullsibQTLis anRopen source package withCandRsource code (GPLv3). It is multiplatform and can be installed fromhttps://github.com/augusto-garcia/fullsibQTL.
IgG Antibodies against SARS-CoV-2 Correlate with Days from Symptom Onset, Viral Load and IL-10
Authors: Young Mary K. , Kornmeier Christine , Carpenter Rebecca M. , Natale Nick R. , Sasson Jennifer M. , Solga Michael D. , Mathers Amy J. , Poulter Melinda D. , Qiang Xiao , Petri William A.
Published: December 2020

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic of the respiratory disease coronavirus disease 2019 (COVID-19). Antibody testing is essential to identify persons exposed to the virus and potentially in predicting disease immunity. 183 COVID-19 patients (68 of whom required mechanical ventilation) and 41 controls were tested for plasma IgG, IgA and IgM against the SARS-CoV-2 S1, S2, receptor binding domain (RBD) and N proteins using the MILLIPLEX® SARS-CoV-2 Antigen Panel. Plasma cytokines were concurrently measured using the MILLIPLEX® MAP Human Cytokine/Chemokine/Growth Factor Panel A. As expected the 183 COVID-19 positive patients had high levels of IgG, IgA and IgM anti-SARS-CoV-2 antibodies against each of the viral proteins. Sensitivity of anti-S1 IgG increased from 60% to 93% one week after symptom onset. S1-IgG and S1-IgA had specificities of 98% compared to the 41 COVID-19 negative patients. The 68 ventilated COVID-19 positive patients had higher antibody levels than the 115 COVID-19 positive patients who were not ventilated. IgG antibody levels against S1 protein had the strongest positive correlation to days from symptom onset. There were no statistically significant differences in IgG, IgA and IgM antibodies against S1 based on age. We found that patients with the highest levels of anti-SARS-CoV-2 antibodies had the lowest viral load in the nasopharynx. Finally there was a correlation of high plasma IL-10 with low anti-SARS-CoV-2 antibodies. Anti-SARS-CoV-2 antibody levels, as measured by a novel antigen panel, increased within days after symptom onset, achieving > 90% sensitivity and specificity within one week, and were highest in patients who required mechanical ventilation. Antibody levels were inversely associated with viral load but did not differ as a function of age. The correlation of high IL-10 with low antibody response suggests a potentially suppressive role of this cytokine in the humoral immune response in COVID-19.
YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data
Authors: Khaki Saeed , Pham Hieu , Wang Lizhi
Published: December 2020

Abstract

Large scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.
Epidemic Progression and Vaccination in a Heterogeneous Population. Application to the Covid-19 epidemic
Authors: Volpert Vitaly , Banerjee Malay , Sharma Swarnali
Published: December 2020

The paper is devoted to a compartmental epidemiological model of infection progression in a heterogeneous population which consists of two groups with high disease transmission (HT) and low disease transmission (LT) potentials. Final size and duration of epidemic, the total and current maximal number of infected individuals are estimated depending on the structure of the population. It is shown that with the same basic reproduction numberR0in the beginning of epidemic, its further progression depends on the ratio between the two groups. Therefore, fitting the data in the beginning of epidemic and the determination ofR0are not sufficient to predict its long time behaviour. Available data on the Covid-19 epidemic allows the estimation of the proportion of the HT and LT groups. Estimated structure of the population is used for the investigation of the influence of vaccination on further epidemic development. The result of vaccination strongly depends on the proportion of vaccinated individuals between the two groups. Vaccination of the HT group acts to stop the epidemic and essentially decreases the total number of infected individuals at the end of epidemic and the current maximal number of infected individuals while vaccination of the LT group only acts to protect vaccinated individuals from further infection.
Comparing Decision Tree-Based Ensemble Machine Learning Models for COVID-19 Death Probability Profiling
Authors: Gonçalves Carlos Pedro , Rouco José
Published: December 2020

We compare the performance of major decision tree-based ensemble machine learning models on the task of COVID-19 death probability prediction, conditional on three risk factors: age group, sex and underlying comorbidity or disease, using the US Centers for Disease Control and Prevention (CDC)’s COVID-19 case surveillance dataset. To evaluate the impact of the three risk factors on COVID-19 death probability, we extract and analyze the conditional probability profile produced by the best performer. The results show the presence of an exponential rise in death probability from COVID-19 with the age group, with males exhibiting a higher exponential growth rate than females, an effect that is stronger when an underlying comorbidity or disease is present, which also acts as an accelerator of COVID-19 death probability rise for both male and female subjects. The results are discussed in connection to healthcare and epidemiological concerns and in the degree to which they reinforce findings coming from other studies on COVID-19.
Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials
Authors: Hazlett Chad , Wulf David Ami , Pasaniuc Bogdan , Arah Onyebuchi A. , Erlandson Kristine M. , Montague Brian T.
Published: December 2020

Objectives

To investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.

Design

Phenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality.

Setting and participants

Data from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200).

Outcome measure

14-day mortality, with a secondary outcome of 28-day mortality.

Results

Hydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes—an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%.

Conclusions

The assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.
Persistent Homology of Tumor CT Scans is Associated with Survival In Lung Cancer
Authors: Somasundaram Eashwar , Litzler Adam , Wadhwa Raoul R. , Barker-Clarke Rowan , Scott Jacob G.
Published: December 2020

Purpose

Radiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall topological structure of the data. This niche can be filled by persistent homology, a form of topological data analysis that analyzes high-level structure. We hypothesized that persistent homology could be applied to lung tumor scans and analyze clinical outcomes.

Methods

We obtained segmented computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. For each scan, a cubical complex filtration based on Hounsfield units was generated. We calculated a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. This curve’s first moment of distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model.

Results

After controlling for tumor image size, age, and stage, the first moment of the 0D topological feature curve was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). The patients in our study with the lowest first moment scores had significantly better survival (1238 days; 95% CI = 936-1599) compared to the patients with the highest first moment scores (429 days; 95% CI = 326-601; p = .0015).

Conclusions

We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.
Assessing the feasibility of Nipah vaccine efficacy trials based on previous outbreaks in Bangladesh
Authors: Nikolay Birgit , Lipsitch Marc , Rahman Mahmudur , Luby Stephen P. , Salje Henrik , Gurley Emily S. , Cauchemez Simon
Published: December 2020

Background

Nipah virus (NiV) is an emerging, bat-borne pathogen that can be transmitted from person-to-person. Vaccines are currently being developed for NiV, and studies funded to evaluate their safety and immunogenicity, so that they could possibly be used to contain outbreaks. An important unanswered question is whether it will be possible to evaluate the efficacy of vaccine candidates in phase III clinical trials in a context where spillovers from the zoonotic reservoir are infrequent and associated with small outbreaks. The objective of this study was to investigate the feasibility of conducting a phase III vaccine trial in Bangladesh, the only country reporting regularly NiV cases.

Methods

We used simulations based on previously observed NiV cases from Bangladesh, an assumed vaccine efficacy of 90% and other NiV vaccine target characteristics, to compare three vaccination study designs: (i) cluster randomized ring vaccination, (ii) cluster randomized mass vaccination, and (iii) an observational case-control study design.

Results

The simulations showed that, assuming a ramp-up period of 10 days and a mean hospitalization delay of 4 days, it would take 516 years and over 163,000 vaccine doses to run a ring vaccination trial under current epidemic conditions. A cluster-randomized trial in the two most affected districts would take 81 years and 2.3 million vaccine doses. An observational case-control design in these two districts would require seven years and 2.5 million vaccine doses.

Discussion

Without a change in the epidemiology of NiV, ring vaccination or cluster-randomized trials are unlikely to be completed within a reasonable time window. In this light, the remaining options are: (i) not conducting a phase III trial until the epidemiology of NiV changes, (ii) identifying alternative ways to licensure such as observational studies or controlled studies in animals such as in the US Food and Drug Administration’s (FDA) Animal Rule.
Using Relion software within Scipion framework
Authors: Sharov Grigory , Morado Dustin R. , Carroni Marta , de la Rosa-Trevín José Miguel
Published: December 2020

Scipion is a modular image processing framework integrating several software packages under a unified interface while taking care of file formats and conversions. Here new developments and capabilities of the Scipion plugin for the Relion software are presented and illustrated with the image processing pipeline of published data. The user interfaces of Scipion and Relion are compared and the key differences highlighted, allowing this manuscript to be used as a guide for both new and experienced users of these software. Different streaming image processing options are also discussed demonstrating the flexibility of the Scipion framework.

Synopsis

An overview of the Scipion plugin for the Relion software is presented and various capabilities of image processing within Scipion framework are discussed.
Efficient inhibition of SARS-CoV-2 strains by a novel ACE2-IgG4-Fc fusion protein with a stabilized hinge region
Authors: Svilenov Hristo L. , Sacherl Julia , Reiter Alwin , Wolff Lisa , Chen Cho-Chin , Stern Marcel , Wachs Frank-Peter , Simonavicius Nicole , Pippig Susanne , Wolschin Florian , Buchner Johannes , Brockmeyer Carsten , Protzer Ulrike
Published: December 2020

The novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) enters its host cells after binding to the angiotensin-converting enzyme 2 (ACE2)viaits spike glycoprotein. This interaction is critical for virus entry and virus-host membrane fusion. Soluble ACE2 ectodomains bind and neutralize the virus but the shortin vivohalf-lives of soluble ACE2 limits its therapeutic use. Fusion of the fragment crystallizable (Fc) part of human immunoglobulin G (IgG) to the ACE2 ectodomain can prolong thein vivohalf-life but bears the risk of unwanted Fc-receptor activation and antibody-dependent disease enhancement. Here, we describe optimized ACE2-Fc fusion constructs that avoid Fc-receptor binding by using IgG4-Fc as a fusion partner. The engineered ACE2-IgG4-Fc fusion proteins described herein exhibit promising pharmaceutical properties and a broad antiviral activity at single-digit nanomolar concentration. In addition, they allow to maintain the beneficial enzymatic activity of ACE2 and thus are very promising candidate antivirals broadly acting against coronaviruses.
Deciphering spatio-temporal fluorescence changes using multi-threshold event detection (MTED)
Authors: Müller Franziska E. , Cherkas Volodymyr , Stopper Gebhard , Caudal Laura C. , Stopper Laura , Kirchhoff Frank , Henneberger Christian , Ponimaskin Evgeni G. , Zeug Andre
Published: December 2020

Recent achievements in indicator optimization and imaging techniques promote the exploration of Ca2+ activity patterns as a main second messenger in many organs. Astrocytes are important regulators of brain activity and well known for their Ca2+-dependent modulation of neurons. However, standardized methods to analyze and interpret Ca2+ activity recordings are missing and hindering global comparisons. Here, we present a biophysics-based concept to analyze Ca2+signals, which includes multiple thresholds and provides the experimenter with a comprehensive toolbox for a differentiated and in-depth characterization of Ca2+ signals. We analyzed various ex vivo and in vivo imaging datasets and verify the validity of our multi-threshold event detection (MTED) algorithm across Ca2+ indicators, imaging setups, and model systems from primary cell culture to awake, head-fixed mice. Applying our MTED concept enables standardized analysis and advances research using optical readouts of cellular activity to decrypt brain function. It allowed us to obtain new insights into the complex dependence of Ca2+activity patterns on temperature and neuronal activity.

Highlights

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We present a robust pixel-based algorithm to analyze multidimensional fluorescence data.

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Automated multiple-threshold analysis accurately quantifies changes in fluorescence across magnitudes.

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It characterizes the complexity of dynamic and overlapping activity patterns of Ca2+ activity of astrocytes in vitro, in situ, and in vivo.

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Its application provides quantitative parameters how temperature and neuronal activity determine astrocytic Ca2+ activity.
Machine learning for analysis of active stand responses in older adults with vasovagal syncope
Authors: Kwok Michelle , Nolan Hugh , Fan Chie Wei , O’Dwyer Clodagh , Kenny Rose A , Finucane Ciarán
Published: December 2020

Objectives

To assess 1) differences in the hemodynamic response to the active stand test in older adults with a clinical diagnosis of vasovagal syncope compared to age-matched controls 2) if the active stand test combined with machine learning approaches can be used to identify the presence of vasovagal syncope in older adults.

Approach

Adults aged 50 and over (Vasovagal Syncope N=46 Age=66.9±10.3; Control N=86 Age=65.3±9.5) completed an active stand test. Multiple features were extracted to characterize the hemodynamic responses to the active stand test and were compared between groups. Classification was performed using machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, support vector machine and an ensemble majority vote classifier.

Main Results

Subjects with vasovagal syncope demonstrated a higher resting (supine) heart rate (69.8±13.1 bpm vs 63.3±12.1 bpm; P=0.007), a smaller initial systolic blood pressure drop (−20.2±20.1% vs −27.3±17.5%; P=0.005), larger drops in stroke volume (−14.7±24.0% vs −2.7±23.3%; P=0.010) and cardiac output (−6.4±18.5% vs 5.8±22.3%;P<0.001) and a larger increase in total peripheral resistance (8.1±30.4% vs −6.03±22.8%; P=0.002) compared to controls. A majority vote classifier identified the presence of vasovagal syncope with 82.6% sensitivity, 76.8% specificity, and average accuracy of 78.9%.

Significance

Older adults with vasovagal syncope display a unique hemodynamic and autonomic response to active standing characterized by relative autonomic hypersensitivity and larger drops in cardiac output compared to age-matched controls. With suitable machine learning algorithms, the active stand test holds the potential to be used to screen older adults for reflex syncopes and hypotensive susceptibility potentially reducing test time, cost, and patient discomfort. More broadly this paper presents a machine learning framework to support use of the active stand test for classification of clinical outcomes of interest.
Seroprevalence of SARS-CoV-2 Antibodies in Seattle, Washington: October 2019-April 2020
Authors: McCulloch Denise J , Jackson Michael L , Hughes James P , Lester Sandra , Mills Lisa , Freeman Brandi , Rasheed Mohammad Ata Ut , Thornburg Natalie L , Chu Helen Y
Published: December 2020

Estimating prevalence of SARS-CoV-2 antibodies is important to determine disease burden. We tested residual samples from 763 Seattle-area adults for SARS-CoV-2 antibodies. Prevalence rose from 0% to 1.2% between October 2019-April 2020, suggesting a small percentage of this metropolitan-area cohort had been infected with SARS-CoV-2 at that time.