bluuebunny/crossref_metadata_2025 huggingface dataset:
Septicaemia of subterranean termites
Coptotermes curvignathus
caused by disturbance of bacteria isolated from termite gut and its foraging pathways
Authors: Chin Kit Ling , H'ng Paik San , Wong Wan Zhen , Lee Chuan Li , Khoo Pui San , Luqman Abdullah Chuah , Ashaari Zaidon , Gandaseca Seca
Published: August 2020

Microbial pathogens continue to attract a great deal of attention to manage the termite population. Every bacterium has its own mode of action and in fact, the mechanisms used by bacteria to attack termites remain elusive at the moment. Hence, the objective of this study was to evaluate the susceptibility of subterranean termites
Coptotermes curvignathus
to opportunistic pathogens using culturable aerobic bacteria isolated from the termite gut and its foraging pathways. Bacterial suspensions were prepared in concentrations of 10
3
, 10
6
and 10
9
colony-forming units (CFU) ml
−1
and introduced to the termites via oral-contact and physical contact treatment. The data show that contact method acted slower and gave lower mortality, compared to the oral-contact method.
Coptotermes curvignathus
were highly susceptible to
Serratia marcescens and Pseudomonas aeruginosa
.
Serratia marcescens
showed the highest mortality percentage of 68% and 54% at bacterial concentration of 10
9
CFU ml
−1
via oral-contact and contact method, respectively.
Serratia marcescens
was also defined as the bacteria with the highest ability to induce the high mortality of
C. curvignathus
with the lowest concentration of bacterial suspension at a given time under laboratory condition. The results of this study indicate that
P. aeruginosa
and
S. marcescens
in particular may be attractive candidates worth further examination as a possible biocontrol agent against
C. curvignathus
in the field and to evaluate environmental and ecological risks of the biocontrol.
Listening forward: approaching marine biodiversity assessments using acoustic methods
Authors: Mooney T. Aran , Di Iorio Lucia , Lammers Marc , Lin Tzu-Hao , Nedelec Sophie L. , Parsons Miles , Radford Craig , Urban Ed , Stanley Jenni
Published: August 2020

Ecosystems and the communities they support are changing at alarmingly rapid rates. Tracking species diversity is vital to managing these stressed habitats. Yet, quantifying and monitoring biodiversity is often challenging, especially in ocean habitats. Given that many animals make sounds, these cues travel efficiently under water, and emerging technologies are increasingly cost-effective, passive acoustics (a long-standing ocean observation method) is now a potential means of quantifying and monitoring marine biodiversity. Properly applying acoustics for biodiversity assessments is vital. Our goal here is to provide a timely consideration of emerging methods using passive acoustics to measure marine biodiversity. We provide a summary of the brief history of using passive acoustics to assess marine biodiversity and community structure, a critical assessment of the challenges faced, and outline recommended practices and considerations for acoustic biodiversity measurements. We focused on temperate and tropical seas, where much of the acoustic biodiversity work has been conducted. Overall, we suggest a cautious approach to applying current acoustic indices to assess marine biodiversity. Key needs are preliminary data and sampling sufficiently to capture the patterns and variability of a habitat. Yet with new analytical tools including source separation and supervised machine learning, there is substantial promise in marine acoustic diversity assessment methods.
Arthrobacter yangruifuii sp. nov. and Arthrobacter zhaoguopingii sp. nov., two new members of the genus Arthrobacter
Authors: Ge Yajun , Tao Yuanmeihui , Yang Jing , Lai Xin-He , Jin Dong , Lu Shan , Huang Ying , Luo Xuelian , Zhou Juan , Zhang Xiaoyan , Xu Jianguo
Published: August 2020

Four unknown strains belonging to the genus

Arthrobacter

were isolated from plateau wildlife on the Qinghai–Tibet Plateau of PR China. Phylogenetic analysis based on 16S rRNA gene sequences showed that the four isolates were separated into two clusters. Cluster I (strains 785T and 208) had the greatest 16S rRNA gene sequence similarity to

Arthrobacter citreus

(98.6 and 98.7 %, respectively),

Arthrobacter luteolus

(98.0 and 98.1%, respectively),

Arthrobacter gandavensis

(97.9 and 98.0 %, respectively) and

Arthrobacter koreensis

(97.6 and 97.7 %, respectively). Likewise, cluster II (strains J391T and J915) had the highest sequence similarity to

Arthrobacter ruber

(98.6 and 98.3 %, respectively) and

Arthrobacter agilis

(98.1 and 97.9  %, respectively). Average nucleotide identity and the digital DNA–DNA hybridization values illustrated that the two type strains, 785T and J391T, represented two separate novel species that are distinct from all currently recognized species in the genus

Arthrobacter

. These strains had DNA G+C contents of 66.0–66.1 mol% (cluster I) and 68.0 mol% (cluster II). The chemotaxonomic properties of strains 785T and J391T were in line with those of the genus

Arthrobacter

: anteiso-C15:0 (79.3 and 40.8 %, respectively) as the major cellular fatty acid, MK-8(H2) (65.8 %) or MK-9(H2) (75.6 %) as the predominant respiratory quinone, a polar lipid profile comprising diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, glycolipids and phospholipid, and A3α or A4α as the cell wall peptidoglycan type. On the basis of our results, two novel species in the genus

Arthrobacter

are proposed, namely Arthrobacter yangruifuii sp. nov. (type strain, 785T=CGMCC 1.16725T=GDMCC 1.1592T=JCM 33491T) and Arthrobacter zhaoguopingii sp. nov. (type strain, J391T=CGMCC 1.17382T=GDMCC 1.1667T=JCM 33841T).
Symbiosis islands of Loteae-nodulating Mesorhizobium comprise three radiating lineages with concordant nod gene complements and nodulation host-range groupings
Authors: Perry Benjamin J. , Sullivan John T. , Colombi Elena , Murphy Riley J.T. , Ramsay Joshua P. , Ronson Clive W.
Published: August 2020

Mesorhizobium

is a genus of soil bacteria, some isolates of which form an endosymbiotic relationship with diverse legumes of the Loteae tribe. The symbiotic genes of these mesorhizobia are generally carried on integrative and conjugative elements termed symbiosis islands (ICESyms).

Mesorhizobium

strains that nodulate Lotus spp. have been divided into host-range groupings. Group I (GI) strains nodulate L. corniculatus and L. japonicus ecotype Gifu, while group II (GII) strains have a broader host range, which includes L. pedunculatus. To identify the basis of this extended host range, and better understand

Mesorhizobium

and ICESym genomics, the genomes of eight

Mesorhizobium

strains were completed using hybrid long- and short-read assembly. Bioinformatic comparison with previously sequenced mesorhizobia genomes indicated host range was not predicted by

Mesorhizobium

genospecies but rather by the evolutionary relationship between ICESym symbiotic regions. Three radiating lineages of Loteae ICESyms were identified on this basis, which correlate with Lotus spp. host-range grouping and have lineage-specific nod gene complements. Pangenomic analysis of the completed GI and GII ICESyms identified 155 core genes (on average 30.1 % of a given ICESym). Individual GI or GII ICESyms carried diverse accessory genes with an average of 34.6 % of genes unique to a given ICESym. Identification and comparative analysis of NodD symbiotic regulatory motifs – nod boxes – identified 21 branches across the NodD regulons. Four of these branches were associated with seven genes unique to the five GII ICESyms. The nod boxes preceding the host-range gene nodZ in GI and GII ICESyms were disparate, suggesting regulation of nodZ may differ between GI and GII ICESyms. The broad host-range determinant(s) of GII ICESyms that confer nodulation of L. pedunculatus are likely present amongst the 53 GII-unique genes identified.
FEIBA® Reversal for Apixaban and Rivaroxaban in Patients with Intracranial and Non-intracranial Hemorrhages
Authors: Hunt Aleah R. , Coffeen Shawn N. , Shiltz Dane L. , Ice Calvin , Parker Jessica
Published: August 2020

Background

Since FDA approvals, apixaban and rivaroxaban use has steadily increased. Currently, FEIBA® has been used off-label for factor Xa inhibitor reversal yet there are limited studies to support this practice. Therefore, additional safety and effectiveness data is needed for apixaban and rivaroxaban reversal in patients with an associated bleeding event.

Methods

The following retrospective study evaluated patients who received at least one dose of FIEBA® for the reversal of apixaban or rivaroxaban. One hundred forty-seven patients with an acute bleed were evaluated. The primary study outcome sought to determine the percentage of patients who achieved excellent or good hemostatic effectiveness within 12 hours of FEIBA® administration. The primary safety outcomes assessed the percent of patients who experienced an inpatient adverse event defined by thrombosis or mortality during hospital admission post-FEIBA® administration.

Results

Among the 147 patients evaluated, 58 experienced an intracranial hemorrhage (ICH) and 89 experienced a non-ICH bleeding event. One hundred fifteen patients (78%) achieved excellent or good hemostasis. Three patients (2%) experienced a thrombotic complication while another 3 patients (2%) had a hemorrhagic complication. A total of 15 patients (10%) experienced in-hospital mortality following FEIBA® administration.

Conclusion

This retrospective study supports the safety and effectiveness of FEIBA® for the management of acute bleeding events secondary to apixaban and rivaroxaban. FEIBA® achieved excellent or good (collectively defined as effective) hemostasis similarly for ICH and non-ICH bleeding events in patients receiving apixaban or rivaroxaban. Furthermore, the thromboembolism outcomes associated with FEIBA® were minimal. The effectiveness and safety outcomes support FEIBA® as a plausible apixaban and rivaroxaban reversal agent, notably among patients experiencing ICH.
IntracellularStaphylococcus aureusperturbs the host cell Ca2+-homeostasis to promote cell death
Authors: Stelzner Kathrin , Winkler Ann-Cathrin , Chunguang Liang , Ade Carsten P. , Dandekar Thomas , Fraunholz Martin J. , Rudel Thomas
Published: August 2020

The opportunistic human pathogenStaphylococcus aureuscauses serious infectious diseases ranging from superficial skin and soft tissue infections to necrotizing pneumonia and sepsis. While classically regarded as extracellular pathogen,S. aureusis able to invade and survive within human cells. Host cell exit is associated with cell death, tissue destruction and spread of infection. The exact molecular mechanism employed byS. aureusto escape the host cell is still unclear. In this study, we performed a genome-wide shRNA screen and identified the calcium signaling pathway to be involved in intracellular infection.S. aureusinduced a massive cytosolic Ca2+-increase in epithelial host cells after invasion and intracellular replication of the pathogen. This was paralleled by decrease in endoplasmic reticulum Ca2+-concentration. Additionally, calcium ions from the extracellular space contributed to the cytosolic Ca2+-increase. As a consequence, we observed that the cytoplasmic Ca2+-rise led to increase in mitochondrial Ca2+-concentration, the activation of calpains and caspases and eventually to cell lysis ofS. aureus-infected cells. Our study therefore suggests that intracellularS. aureusdisturbs the host cell Ca2+-homeostasis and induces cytoplasmic Ca2+-overload, which results in both apoptotic and necrotic cell death in parallel or succession.

Importance

Despite being regarded as an extracellular bacterium, the pathogenStaphylococcus aureuscan invade and survive within human cells. The intracellular niche is considered as hide-out from the host immune system and antibiotic treatment and allows bacterial proliferation. Subsequently, the intracellular bacterium induces host cell death, which may facilitate spread of infection and tissue destruction. So far, host cell factors exploited by intracellularS. aureusto promote cell death are only poorly characterized. We performed a genome-wide screen and found the calcium signaling pathway to play a role inS. aureusinvasion and cytotoxicity. The intracellular bacterium induces a cytoplasmic and mitochondrial Ca2+-overload, which results in host cell death. Thus, this study firstly showed how an intracellular bacterium perturbs the host cell Ca2+-homeostasis.
Biophysical characterization of the ETV6 PNT domain polymerization interfaces
Authors: Gerak Chloe A. N. , Cho Sophia Y. , Kolesnikov Maxim , Okon Mark , Murphy Michael E. P. , Sessions Richard B. , Roberge Michel , McIntosh Lawrence P.
Published: August 2020

ETV6 is an ETS family transcriptional repressor that self-associates by its PNT domain to facilitate cooperative DNA binding. Chromosomal translocations frequently generate constitutively active oncoproteins with the ETV6 PNT domain fused to the kinase domain of one of many protein tyrosine kinases. Although an attractive target for therapeutic intervention, the propensity of the ETV6 PNT domain to polymerize via the tight head-to-tail association of two relatively flat interfaces makes it challenging to identify suitable small molecule inhibitors of this protein-protein interaction. Herein we provide a comprehensive biophysical characterization of the ETV6 PNT domain interaction interfaces to aid future drug discovery efforts and help define the mechanisms by which its self-association mediates transcriptional repression. Using NMR spectroscopy, X-ray crystallography, and molecular dynamics simulations, we demonstrate that ETV6 PNT domain variants with monomerizing mutations adopt very stable helical bundle folds that do not change in conformation upon self-association. Amide hydrogen exchange and surface plasmon resonance-monitored alanine scanning mutagenesis studies identified hot spot regions within the self-association interfaces. These regions include both central hydrophobic residues and flanking salt-bridging residues. Collectively, these studies indicate that small molecules targeted to these hydrophobic or charged regions within the relatively rigid interfaces could potentially serve as orthosteric inhibitors of ETV6 PNT domain polymerization.
Dementia Risk and Dynamic Response to Exercise: Methodology for an Acute Exericise Clinical Trial
Authors: White Dreu , John Casey S , Kucera Ashley , Truver Bryce , Lepping Rebecca J , Lee Phil , Martin Laura , Billinger Sandra A , Burns Jeffrey M , Morris Jill K , Vidoni Eric D
Published: August 2020

Background

Exercise likely has numerous, meaningful benefits for brain and cognition. However, those benefits and their causes remain imprecisely defined, especially in the context of cognitive disorders associated with aging, such as Alzheimer’s disease (AD). If the brain does benefit from exercise it does so primarily through exposure to brief, “acute” exposures to exercise over a lifetime. Methods: The Dementia Risk and Dynamic Response to Exercise (DYNAMIC) clinical trial seeks to characterize the acute exercise response in cerebral perfusion, and circulating neurotrophic factors in older adults with and without the apolipoprotein e4 genotype (APOE4), the strongest genetic predictor or sporadic, late onset AD. DYNAMIC will enroll 60 older adults into a single moderate intensity bout of exercise intervention. We will measure pre- and post-exercise cerebral blood flow using arterial spin labeling, and neurotrophic factors. We expect that APOE4 carriers will have poor CBF regulation, i.e. slower return to baseline perfusion after exercise, and will demonstrate blunted neurotrophic response to exercise, with concentrations of neurotrophic factors positively correlating with CBF regulation. If exercise-induced changes in perfusion and circulating factors can be detected, DYNAMIC will contribute to our understanding of exercise-induced brain change and potential biomarker outcomes of exercise interventions. Results: Preliminary proof-of-concept findings on 7 older adults and 9 younger adults. We have found that this experimental method can capture CBF and neurotrophic response over a time course, and best practices following exercise. Conclusions: This methodology will provide important insight into acute exercise response and potential directions for clinical trial outcomes. ClinicalTrials.govNCT04009629
Deep learning predicts post-surgical recurrence of hepatocellular carcinoma from digital whole-slide images
Authors: Yamashita Rikiya , Long Jin , Saleem Atif , Rubin Daniel L. , Shen Jeanne
Published: August 2020

Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still have tumor recurrence at 5 years post-surgery. Routine hematoxylin and eosin (H&E)-stained histopathology slides may contain morphologic features associated with tumor recurrence. In this study, we developed and independently validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary surgical resection, directly from H&E-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved a concordance index of 0.724 on a held-out internal test set of 53 patients, and 0.683 on an external test set of 198 patients, exceeding the performance of standard staging using the American Joint Committee on Cancer (AJCC)/International Union against Cancer (UICC) Tumor-Node-Metastasis (TNM) classification system, on both the internal and external test cohorts (p = 0.018 and 0.025, respectively). We observed statistically significant differences in the survival distributions between low- and high-risk subgroups, as stratified by the risk scores predicted by HCC-SurvNet on both the internal and external test sets (log-rank p-value: 0.0013 and < 0.0001, respectively). On multivariable Cox proportional hazards analysis, the risk score was an independent risk factor for post-surgical recurrence, on both the internal (hazard ratio (HR) = 7.44 (95% CI: 1.60, 34.6), p = 0.0105) and external (HR = 2.37 (95% CI: 1.27, 4.43), p = 0.0069) test sets. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods, and help refine the clinical management of patients undergoing primary surgical resection for HCC.
The Association between Sleep Quality and Type Two Diabetes at 20 Year Follow-up in the Southall And Brent REvisited (SABRE) Cohort: a Tri-ethnic Analysis
Authors: Ong Zhen L. , Chaturvedi Nishi , Tillin Therese , Dale Caroline , Garfield Victoria
Published: August 2020

Objective

The risk of developing type 2 diabetes associated with poor sleep quality is comparable to that of traditional risk factors (e.g. overweight, physical inactivity). In the United Kingdom, these traditional risk factors could not explain the two to three-fold excess risks in South Asian and African Caribbean men compared to Europeans. This study investigates the (i)the association between mid-life sleep quality and later-life type 2 diabetes risk and (ii)a potential modifying effect of ethnicity.

Research Design and Methods

The Southall and Brent REvisited (SABRE) cohort comprised Europeans, South Asians, and African Caribbeans (median follow-up = 19 years). Complete case analysis was performed on 2190 participants without diabetes at baseline (age= 51.7± 7SD). Competing risks regressions were used to estimate the hazard ratios (HRs) of developing type 2 diabetes associated with four self-reported baseline sleep exposures (difficulty falling asleep, early morning waking, waking up tired and snoring) while adjusting for confounders. Modifying effects of ethnicity were analysed by (i) testing for interactions and (ii) performing ethnicity-stratified analysis.

Results

Snoring was strongly associated with increased type 2 diabetes risk but only among South Asians in a fully-adjusted model (HR 1.42, 95%CI=1.08-1.85, P=0.011). Our results revealed no elevated risk for any of the sleep exposures across all three ethnic groups.

Conclusions

The association between snoring and type 2 diabetes appeared to be modified by ethnicity, with South Asians at greatest risk.
Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events
Authors: Lemieux Jacob E. , Siddle Katherine J. , Shaw Bennett M. , Loreth Christine , Schaffner Stephen F. , Gladden-Young Adrianne , Adams Gordon , Fink Timelia , Tomkins-Tinch Christopher H. , Krasilnikova Lydia A. , DeRuff Katherine C. , Rudy Melissa , Bauer Matthew R. , Lagerborg Kim A. , Normandin Erica , Chapman Sinead B. , Reilly Steven K. , Anahtar Melis N. , Lin Aaron E. , Carter Amber , Myhrvold Cameron , Kemball Molly E. , Chaluvadi Sushma , Cusick Caroline , Flowers Katelyn , Neumann Anna , Cerrato Felecia , Farhat Maha , Slater Damien , Harris Jason B. , Branda John , Hooper David , Gaeta Jessie M. , Baggett Travis P. , O’Connell James , Gnirke Andreas , Lieberman Tami D. , Philippakis Anthony , Burns Meagan , Brown Catherine M. , Luban Jeremy , Ryan Edward T. , Turbett Sarah E. , LaRocque Regina C. , Hanage William P. , Gallagher Glen R. , Madoff Lawrence C. , Smole Sandra , Pierce Virginia M. , Rosenberg Eric , Sabeti Pardis C. , Park Daniel J. , Maclnnis Bronwyn L.
Published: August 2020

SARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and analyzed 772 complete SARS-CoV-2 genomes from the region, including nearly all confirmed cases within the first week of the epidemic and hundreds of cases from major outbreaks at a conference, a nursing facility, and among homeless shelter guests and staff. The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission, including outbreaks in homeless populations, and was exported to several other domestic and international sites. The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into MA early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data.
Population pharmacokinetic modeling of pyrotinib in patients with HER2-positive advanced or metastatic breast cancer
Authors: Wen Hai-ni , Liu Yi-xi , Xu Da , Zhao Kai-jing , Jiao Zheng
Published: August 2020

Objective

Pyrotinib, a novel oral irreversible dual pan-ErbB tyrosine kinase inhibitor (TKI), has been approved in China for the treatment of HER2-positive advanced or metastatic breast cancer. This study aimed to conduct a population pharmacokinetics (PK) analysis of pyrotinib and to evaluate the impact of certain HER2-positive breast cancer patient characteristics on pyrotinib’s PK.

Method

A total of 1152 samples, provided by 59 adult female patients from two phase I clinical trials, were analyzed by nonlinear mixed-effects modeling. Monte Carlo simulation was conducted to assess the impact of covariates following exposure to pyrotinib.

Results

The PK of pyrotinib was adequately described by a one-compartment model with first-order absorption and elimination. Patient’s age and total protein levels can affect pyrotinib’s apparent volume of distribution, and concomitant use of montmorillonite powder had significant effects on the bioavailability of pyrotinib. No PK interactions were observed between capecitabine and pyrotinib.

Conclusion

In this study, a population PK model of pyrotinib was developed to determine the influence of patient characteristics on the PK of pyrotinib. While patient age and total protein levels can significantly affect the apparent distribution volume of pyrotinib, the magnitude of the impact was limited, thus no dosage adjustment was recommended. Furthermore, concomitant use of montmorillonite powder for diarrhea can decrease the bioavailability of pyrotinib by 50.3%.
Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning
Authors: Navlakha Saket , Morjaria Sejal , Perez-Johnston Rocio , Zhang Allen , Taur Ying
Published: August 2020

Background

Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear.

Methods

We used machine learning algorithms to predict COVID-19 severity in 354 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using clinical variables only collected on or before a patient’s COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support).

Results

Our algorithm classified patients into these classes with an AUROC ranging from 70-85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables — including patient demographics, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type — suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors.

Conclusions

Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.
The Futures of the Pandemic in the USA : A Timed Intervention Model
Authors: Hachtel Gary D.
Published: August 2020

We propose a novel Timed Intervention exended SEIR model for predicting the evolution of the Covid 19 Pandemic in the USA. The model can, by sparameter, assignment, be reduced to the model of Peng et al, which appeared in February, 2020 (Reference [2]). Novel aspects of the proposed model include

Formulation of a “Protected” population P, which can be viewed as a “Sheltered in Place”, unexposed population which, starting at time t = τP, builds up and stores a reservoir of unexposed Population;

This “Protection Intervention” provides the basis for a second Timed Release Intervention: on receiving a “reopening signal” at time t = τR, this second intervention initiates a release of this stored population back into the general population S.;

Selection of model parameters to optimize the approximation of the model up to the present, and then projecting simulation of the model based on the value thus obtained 100,200, 365, and 730 days into the future.

This model shows excellent qualitative results and quantitative results that are good, considering the chosen nationwide scope. These results compare favorably to University of Washington IHME [5] projections, as published Daily on the http://Worldometers.info website. The qualitative and quantitative behavior of all 7 state variables S, P, E, I, Q, R, D will be illustrated and discussed, in this and future further Intervention cycles1
Wastewater sample site selection to estimate geographically-resolved community prevalence of COVID-19: A research protocol
Authors: Yeager Ray A. , Holm Rochelle H. , Saurabh Kumar , Fuqua Joshua L , Talley Daymond , Bhatnagar Aruni , Smith Ted
Published: August 2020

Background

Wastewater monitoring for virus infections within communities can complement conventional clinical surveillance. Currently, most SARS-CoV-2 testing is performed during clinical encounters with symptomatic individuals, and therefore likely underrepresents actual population prevalence. Randomized testing on a regular basis to estimate population-level infection rates is prohibitively costly and is hampered by a range of barriers associated with participation in clinical research. In comparison, community-level fecal monitoring can be performed through wastewater surveillance and can effectively surveil communities with less temporal lag than other surveillance methods. However, epidemiologically-defined protocols for wastewater sample site selection are lacking.

Methods

Herein we describe methods for developing a geographically-resolved population-level wastewater sampling approach in Jefferson County, Kentucky which may have general applicability for cities throughout the United States. This approach was developed by the selection of sampling locations along sewer lines transporting raw wastewater from geographically and demographically distinct areas that correspond with locations where random testing of residents occurs.

Conclusions

Development of this protocol for population-level sampling for SARS-CoV-2 prevalence in wastewater can be utilized to inform consistent wastewater monitoring among cities for up-to-date and geographically-resolved information on COVID-19 prevalence within communities. This information could substantially supplement public health surveillance of COVID-19 and thus serve to better guide targeted mitigation strategies throughout the United States.
Hesitant or not? A global survey of potential acceptance of a COVID-19 vaccine
Authors: Lazarus Jeffrey V , Ratzan Scott , Palayew Adam , Gostin Lawrence O , Larson Heidi J , Rabin Kenneth , Kimball Spencer , El-Mohandes Ayman
Published: August 2020

A number of COVID-19 vaccines are under development, with one or more possibly becoming available in 2021. We conducted a global survey in June 2020 of 13,426 people in 19 countries to determine potential acceptance rates of a COVID-19 vaccine and factors influencing acceptance. We ran univariate logistic regressions to examine the associations with demographic variables. 71.5% reported they would be very or somewhat likely to take a COVID-19 vaccine; 61.4% reported they would accept their employer’s recommendation to take a COVID-19 vaccine. Differences in acceptance across countries ranged from almost 9 in 10 (China) to fewer than 6 in 10 (Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine, and take their employer’s advice to do so. Targeted interventions addressing age, sex, income, and education level are required to increase and sustain public acceptance of a COVID-19 vaccine.
Predicting deseasonalised serum 25 hydroxy vitamin D concentrations in the D-Health Trial: an analysis using boosted regression trees
Authors: Waterhouse Mary , Baxter Catherine , Romero Briony Duarte , McLeod Donald S. A. , English Dallas R. , Armstrong Bruce K. , Clarke Michael W. , Ebeling Peter R. , Hartel Gunter , Kimlin Michael G. , O’Connell Rachel L. , Pham Hai , Harris Rachael M. Rodney , van der Pols Jolieke C. , Venn Alison J. , Webb Penelope M. , Whiteman David C. , Neale Rachel E.
Published: August 2020

Background

The D-Health Trial aims to determine whether monthly high-dose vitamin D supplementation can reduce the mortality rate and prevent cancer. We did not have adequate statistical power for subgroup analyses, so could not justify the high cost of collecting blood samples at baseline. To enable future exploratory analyses stratified by baseline vitamin D status, we developed a model to predict baseline serum 25 hydroxy vitamin D [25(OH)D] concentration.

Methods

We used data and serum 25(OH)D concentrations from participants who gave a blood sample during the trial for compliance monitoring and were randomised to placebo. Data were partitioned into training (80%) and validation (20%) datasets. Deseasonalised serum 25(OH)D concentrations were dichotomised using cut-points of 50 nmol/L, 60 nmol/L and 75 nmol/L. We fitted boosted regression tree models, based on 13 predictors, and evaluated model performance using the validation data.

Results

The training and validation datasets had 1788 (10.5% <50 nmol/L, 23.1% <60 nmol, 48.8 <75 nmol/L) and 447 (11.9% <50 nmol/L, 25.7% <60 nmol/L, and 49.2% <75 nmol/L) samples, respectively. Ambient UV radiation and total intake of vitamin D were the strongest predictors of ‘low’ serum 25(OH)D concentration. The area under the receiver operating characteristic curves were 0.71, 0.70, and 0.66 for cut-points of <50 nmol/L, <60 nmol/L and <75 nmol/L respectively.

Conclusions

We exploited compliance monitoring data to develop models to predict serum 25(OH)D concentration for D-Health participants at baseline. This approach may prove useful in other trial settings where there is an obstacle to exhaustive data collection.
An examination of COVID-19 mitigation efficiency among 23 countries
Authors: Chen Yi-Tui , Yu Shih-Heng , Su Emily Chia-Yu
Published: August 2020

The purpose of this paper is to compare the relative mitigation efficiency of COVID-19 transmission among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). This paper evaluated the mitigation efficiency for each country at each stage by using data envelopment analysis (DEA) tools and analyzed changes in mitigation efficiency across stages. Pearson correlation tests were conducted between each change to examine the impact of efficiency ranks in the previous stage on subsequent stages. An indicator was developed to judge epidemic stability and was applied to practical cases involving lifting travel restrictions and restarting the economy in some countries.

The results showed that Korea and Australia performed with the highest efficiency in preventing the diffusion of COVID-19 for the whole period covering 120 days since the first confirmed case, while the USA ranked at the bottom. China, Japan, Korea and Australia were judged to have recovered from the attack of COVID-19 due to higher epidemic stability.
Precision Prostate Cancer Screening with a Polygenic Risk Score
Authors: Tasa Tõnis , Puustusmaa Mikk , Tõnisson Neeme , Kolk Berit , Padrik Peeter
Published: August 2020

Prostate cancer (PC) is the second-most common type of cancer and the fifth-leading cause of cancer-related death in men worldwide. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with PC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of men according to PRS could be applied in secondary prevention. We aimed to construct a PRS model and to develop a pipeline for personalized prostate cancer screening.

Previously published PRS models for predicting the risk of prostate cancer were collected from the literature. These were validated on the Estonian Biobank (EGC) consisting of a total of 16,390 quality-controlled genotypes with 262 prevalent and 428 incident PC cases and on 209 634 samples in the UK Biobank with 3254 prevalent cases and 6959 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian PC background information, we performed absolute risk simulations and developed individual risk-based clinical follow-up recommendations.

The best-performing PRS included 121 SNPs. The C-index of the Cox regression model associating PC status with PRS was 0.641 (SE = 0.015) with a hazard ratio of 1.65 (95% confidence interval 1.51 – 1.81) on the incident EGC dataset. The model is able to identify individuals with more than a 3-fold risk increase. The risk of an average 45-year old could be attained by individuals between the ages of 41 and 52. A 41-year old male on the 95th percentile has the same risk as an average 45-year old but by age 55, he has attained the same genetic risk as an average 68-year-old.

PRS is a powerful predictor of prostate cancer risk that can be combined with current non-invasive practices of PC screening. We have developed PRS-based recommendations for personalized PSA testing. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.
Meta-analysis shows that context-dependent mating behaviour is inconsistent or weak across animals
Authors: Dougherty Liam R.
Published: August 2020

Animals often need to invest significantly in mating behaviour in order to successfully mate. However, the expression of mating behaviour can be costly, especially in unfavourable environments, so animals are expected to adjust their behaviour in a context-dependent way to mitigate these costs. I systematically searched the literature for studies measuring animal mating behaviour (sexual signalling, response to sexual signals, or the strength of mate choice) in more than one environment, and used a phylogenetically-controlled meta-analysis to identify environmental factors influencing these behaviours. Across 222 studies, the strength of mate choice was significantly context-dependent, and most influenced by population density, population sex ratio, and predation risk. However, the average effect sizes were typically small. The amount of sexual signalling and the strength of response to sexual signals were not significantly related to the environment. Overall, this suggests that the evidence for context-dependent mating behaviour across animals is surprisingly weak.
Deep learning-based molecular morphometrics for kidney biopsies
Authors: Zimmermann Marina , Klaus Martin , Wong Milagros N. , Thebille Ann-Katrin , Gernhold Lukas , Kuppe Christoph , Halder Maurice , Kranz Jennifer , Wanner Nicola , Braun Fabian , Wulf Sonia , Wiech Thorsten , Panzer Ulf , Krebs Christian F. , Hoxha Elion , Kramann Rafael , Huber Tobias B. , Bonn Stefan , Puelles Victor G.
Published: August 2020

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. Previously unknown morphometric signatures of podocyte depletion were identified in patients with ANCA-GN, which allowed patient classification and showed potential for risk stratification in combination with routine clinical tools. Together, our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

Deep learning enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.
Temporal increase in D614G mutation of SARS-CoV-2 in the Middle East and North Africa: Phylogenetic and mutation analysis study
Authors: Sallam Malik , Ababneh Nidaa A. , Dababseh Deema , Bakri Faris G. , Mahafzah Azmi
Published: August 2020

Phylogeny construction can help to reveal evolutionary relatedness among molecular sequences. The spike (S) gene of SARS-CoV-2 is the subject of an immune selective pressure which increases the variability in such region. This study aimed to identify mutations in theSgene among SARS-CoV-2 sequences collected in the Middle East and North Africa (MENA), focusing on the D614G mutation, that has a presumed fitness advantage. Another aim was to analyze theSgene sequences phylogenetically. The SARS-CoV-2Sgene sequences collected in the MENA were retrieved from the GISAID public database, together with its metadata. Mutation analysis was conducted in Molecular Evolutionary Genetics Analysis software. Phylogenetic analysis was done using maximum likelihood (ML) and Bayesian methods. A total of 553 MENA sequences were analyzed and the most frequentSgene mutations included: D614G = 435, Q677H = 8, and V6F = 5. A significant increase in the proportion of D614G was noticed from (63.0%) in February 2020, to (98.5%) in June 2020 (p< 0.001). Two large phylogenetic clusters were identified via ML analysis, which showed an evidence of inter-country mixing of sequences, which dated back to February 8, 2020 and March 15, 2020 (median estimates). The mean evolutionary rate for SARS-CoV-2 was about 6.5 × 10−3substitutions/site/year based on large clusters’ Bayesian analyses. The D614G mutation appeared to be taking over the COVID-19 infections in the MENA. Bayesian analysis suggested that SARS-CoV-2 might have been circulating in MENA earlier than previously reported.
Three-dimensional convolutional autoencoder extracts features of structural brain images with a “diagnostic label-free” approach: Application to schizophrenia datasets
Authors: Yamaguchi Hiroyuki , Hashimoto Yuki , Sugihara Genichi , Miyata Jun , Murai Toshiya , Takahashi Hidehiko , Honda Manabu , Hishimoto Akitoyo , Yamashita Yuichi
Published: August 2020

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS), and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets including 71 SZ patients and 71 HS. The proposed 3D-CAEs were successfully reconstructed into high-resolution 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relevant clinical information. We explored the appropriate hyper parameter range of 3D-CAE, and it was suggested that a model with eight convolution layers might be relevant to extract features for predicting the dose of medication and symptom severity in schizophrenia.
Estimating encounter location distributions from animal tracking data
Authors: Noonan Michael J. , Martinez-Garcia Ricardo , Davis Grace H. , Crofoot Margaret C. , Kays Roland , Hirsch Ben T. , Caillaud Damien , Payne Eric , Sih Andrew , Sinn David L. , Spiegel Orr , Fagan William F. , Fleming Christen H. , Calabrese Justin M.
Published: August 2020

Ecologists have long been interested in linking individual behavior with higher-level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors, and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modeling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounterrateswhile no theoretical framework exists for directly relating individual movement with the spatiallocationsof encounter events in the environment.

Here, we bridge this gap by introducing a new theoretical concept describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open source software, and demonstrate the broad ecological relevance of this novel concept.

We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins (Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the location-specific potential for competition, and/or identify any changes in behaviour that directly result from location-specific encounter probability.

This novel target distribution enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialised data collection protocols. This method is now openly available via the ctmm R package.