bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relevance of circulating hybrid cells as a non-invasive biomarker for myriad solid tumors
Authors: Dietz Matthew S. , Sutton Thomas L. , Walker Brett S. , Gast Charles E. , Zarour Luai , Sengupta Sidharth K. , Swain John R. , Eng Jennifer , Parappilly Michael , Limbach Kristen , Sattler Ariana , Burlingame Erik , Chin Yuki , Gower Austin , Mira Jose L. Montoya , Sapre Ajay , Chiu Yu-Jui , Clayburgh Daniel R. , Pommier SuEllen J. , Cetnar Jeremy P. , Fischer Jared M. , Jaboin Jerry J. , Pommier Rodney F. , Sheppard Brett C. , Tsikitis V. Liana , Skalet Alison H. , Mayo Skye C. , Lopez Charles D. , Gray Joe W. , Mills Gordon B. , Mitri Zahi , Chang Young Hwan , Chin Koei , Wong Melissa H. 
Published: July 2021

AbstractMetastatic progression defines the final stages of tumor evolution and underlies the majority of cancer-related deaths. The heterogeneity in disseminated tumor cell populations capable of seeding and growing in distant organ sites contributes to the development of treatment resistant disease. We recently reported the identification of a novel tumor-derived cell population, circulating hybrid cells (CHCs), harboring attributes from both macrophages and neoplastic cells, including functional characteristics important to metastatic spread. These disseminated hybrids outnumber conventionally defined circulating tumor cells (CTCs) in cancer patients. It is unknown if CHCs represent a generalized cancer mechanism for cell dissemination, or if this population is relevant to the metastatic cascade. Herein, we detect CHCs in the peripheral blood of patients with cancer in myriad disease sites encompassing epithelial and non-epithelial malignancies. Further, we demonstrate that in vivo-derived hybrid cells harbor tumor-initiating capacity in murine cancer models and that CHCs from human breast cancer patients express stem cell antigens, features consistent with the potential to seed and grow at metastatic sites. Finally, we reveal heterogeneity of CHC phenotypes reflect key tumor features, including oncogenic mutations and functional protein expression. Importantly, this novel population of disseminated neoplastic cells opens a new area in cancer biology and renewed opportunity for battling metastatic disease.
Multi-country evaluation of RISK6, a 6-gene blood transcriptomic signature, for tuberculosis diagnosis and treatment monitoring
Authors: Bayaa Rim , Ndiaye Mame Diarra Bousso , Chedid Carole , Kokhreidze Eka , Tukvadze Nestani , Banu Sayera , Uddin Mohammad Khaja Mafij , Biswas Samanta , Nasrin Rumana , Ranaivomanana Paulo , Raherinandrasana Antso Hasina , Rakotonirina Julio , Rasolofo Voahangy , Delogu Giovanni , De Maio Flavio , Goletti Delia , Endtz Hubert , Ader Florence , Hamze Monzer , Ismail Mohamad Bachar , Pouzol Stéphane , Rakotosamimanana Niaina , Hoffmann Jonathan ,   The HINTT working group within the GABRIEL network, Russomando Graciela , Acosta Chyntia Carolina Díaz , Arenas Rossana 
Published: July 2021

AbstractThere is a crucial need for non-sputum-based TB tests. Here, we evaluate the performance of RISK6, a human-blood transcriptomic signature, for TB screening, triage and treatment monitoring. RISK6 performance was also compared to that of two IGRAs: one based on RD1 antigens (QuantiFERON-TB Gold Plus, QFT-P, Qiagen) and one on recombinant M. tuberculosis HBHA expressed in Mycobacterium smegmatis (IGRA-rmsHBHA). In this multicenter prospective nested case–control study conducted in Bangladesh, Georgia, Lebanon and Madagascar, adult non-immunocompromised patients with bacteriologically confirmed active pulmonary TB (ATB), latent TB infection (LTBI) and healthy donors (HD) were enrolled. ATB patients were followed-up during and after treatment. Blood RISK6 scores were assessed using quantitative real-time PCR and evaluated by area under the receiver-operating characteristic curve (ROC AUC). RISK6 performance to discriminate ATB from HD reached an AUC of 0.94 (95% CI 0.89–0.99), with 90.9% sensitivity and 87.8% specificity, thus achieving the minimal WHO target product profile for a non-sputum-based TB screening test. Besides, RISK6 yielded an AUC of 0.93 (95% CI 0.85–1) with 90.9% sensitivity and 88.5% specificity for discriminating ATB from LTBI. Moreover, RISK6 showed higher performance (AUC 0.90, 95% CI 0.85–0.94) than IGRA-rmsHBHA (AUC 0.75, 95% CI 0.69–0.82) to differentiate TB infection stages. Finally, RISK6 signature scores significantly decreased after 2 months of TB treatment and continued to decrease gradually until the end of treatment reaching scores obtained in HD. We confirmed the performance of RISK6 signature as a triage TB test and its utility for treatment monitoring.
Joint DNA-based disaster victim identification
Authors: Vigeland Magnus D. , Egeland Thore 
Published: July 2021

AbstractWe address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.
Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
Authors: Cheng Jack , Liu Hsin-Ping , Lin Wei-Yong , Tsai Fuu-Jen 
Published: July 2021

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.
Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
Authors: Gurudas Sarega , Nugawela Manjula , Prevost A. Toby , Sathish Thirunavukkarasu , Mathur Rohini , Rafferty J. M. , Blighe Kevin , Rajalakshmi Ramachandran , Mohan Anjana R. , Saravanan Jebarani , Majeed Azeem , Mohan Viswanthan , Owens David R. , Robson John , Sivaprasad Sobha ,   the ORNATE India Study Group
Published: July 2021

AbstractPrediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.
Complex effects of macrolide venturicidins on bacterial F-ATPases likely contribute to their action as antibiotic adjuvants
Authors: Milgrom Yakov M. , Duncan Thomas M. 
Published: July 2021

AbstractBacterial energy metabolism is now recognized as a critical factor for the efficacy of antibiotics. The F-type ATPase/ATP synthase (FOF1) is a central player in cellular bioenergetics of bacteria and eukaryotes, and its potential as a selective antibiotic target has been confirmed by the success of bedaquiline in combatting multidrug-resistant tuberculosis. Venturicidin macrolides were initially identified for their antifungal properties and were found to specifically inhibit FOF1 of eukaryotes and bacteria. Venturicidins alone are not effective antibacterials but recently were found to have adjuvant activity, potentiating the efficacy of aminoglycoside antibiotics against several species of resistant bacteria. Here we discovered more complex effects of venturicidins on the ATPase activity of FOF1 in bacterial membranes from Escherichia coli and Pseudomonas aeruginosa. Our major finding is that higher concentrations of venturicidin induce time– and ATP–dependent decoupling of F1-ATPase activity from the venturicidin-inhibited, proton-transporting FO complex. This dysregulated ATPase activity is likely to be a key factor in the depletion of cellular ATP induced by venturicidins in prior studies with P. aeruginosa and Staphylococcus aureus. Further studies of how this functional decoupling occurs could guide development of new antibiotics and/or adjuvants that target the F-type ATPase/ATP synthase.
Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
Authors: Amor Nesrine , Noman Muhammad Tayyab , Petru Michal , Mahmood Aamir , Ismail Adla 
Published: July 2021

AbstractThis paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input–output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.
In search of a Goldilocks zone for credible AI
Authors: Allan Kevin , Oren Nir , Hutchison Jacqui , Martin Douglas 
Published: July 2021

AbstractIf artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal ‘Goldilocks’ zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI’s influence, raising important implications and new directions for research on human–AI interaction.
Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
Authors: da Silva Flavia Alves , Viana Alexandre Pio , Correa Caio Cezar Guedes , Santos Eileen Azevedo , de Oliveira Julie Anne Vieira Salgado , Andrade José Daniel Gomes , Ribeiro Rodrigo Moreira , Glória Leonardo Siqueira 
Published: July 2021

AbstractMarkers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to $$\pi$$
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), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual’s genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.
Effects of freezing rate on structural changes in l-lactate dehydrogenase during the freezing process
Authors: Park Haena , Park Jun-Young , Park Kyung-Min , Chang Pahn-Shick 
Published: July 2021

AbstractFreezing is a common method for improving enzyme storage stability. During the freezing process, the freezing rate is an important parameter that can affect protein stability. However, there is limited information on the denaturation mechanisms and protein conformational changes associated with the freezing rate. In this study, the effects of freezing rate on activity loss and conformational changes in a model enzyme, l-lactate dehydrogenase, were evaluated. Enzyme solutions were frozen at various rates, from 0.2 to 70.6 °C/min, and ice seeding was conducted to reduce supercooling. The results demonstrated that fast freezing results in activity loss, structural changes, and aggregation. The residual activities at freezing rates of 0.2, 12.8, and 70.6 °C/min were 77.6 ± 0.9%, 64.1 ± 0.4%, and 44.8 ± 2.0%, respectively. As the freezing rate increased, the degree of dissociation and unfolding increased significantly, as determined using blue native-polyacrylamide gel electrophoresis and fluorescence spectroscopy. Moreover, a large number of amyloid aggregates were detected in samples frozen at a fast freezing rate (70.6 °C/min). The enzyme inactivation mechanism induced by fast freezing was proposed in terms of increased dehydration at the enzyme surface and an ice/unfroze solution interface, which could be helpful to establish a common understanding of enzyme inactivation during the freezing process.
High-precision and cost-efficient sequencing for real-time COVID-19 surveillance
Authors: Park Sung Yong , Faraci Gina , Ward Pamela M. , Emerson Jane F. , Lee Ha Youn 
Published: July 2021

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.
Analysis of the physical properties of spindle seeds for seed sorting operations
Authors: Kaliniewicz Zdzisław , Anders Andrzej , Markowski Piotr , Tylek Paweł , Owoc Danuta 
Published: July 2021

AbstractThe relationships between the basic physical properties of seeds of selected spindle species were evaluated for the needs of seed sorting operations. Physical properties were measured in the seeds of five spindle species, and the presence of relationships between these attributes was determined in correlation and regression analyses. The average values of the evaluated parameters were determined in the following range: terminal velocity—from 9.2 to 10.3 m s−1, thickness—from 2.57 to 3.26 mm, width—from 2.87 to 3.74 mm, length—from 3.94 to 5.52 mm, angle of external friction—from 20.7° to 24.6°, mass—from 16.5 to 33.8 mg. Spindle seeds were arranged in the following ascending order based on their geometric mean diameter: winged spindle, Hamilton’s spindle, large-winged spindle, broadleaf spindle and European spindle. Spindle seeds should be separated in a sieve equipped with at least two mesh screens with slotted apertures. Depending on the processed spindle species, aperture size should range from ≠ 2.7 to ≠ 3.5 mm in the top screen, and from ≠ 2.4 to ≠ 3.0 mm in the bottom screen.
Reconfigurable and scalable 2,4-and 6-channel plasmonics demultiplexer utilizing symmetrical rectangular resonators containing silver nano-rod defects with FDTD method
Authors: Khani Shiva , Farmani Ali , Mir Ali 
Published: July 2021

AbstractReconfigurable and scalable plasmonics demultiplexers have attracted increasing attention due to its potential applications in the nanophotonics. Therefore, here, a novel method to design compact plasmonic wavelength demultiplexers (DEMUXes) is proposed. The designed structures (two, four, and six-channel DEMUXes) consist of symmetrical rectangular resonators (RRs) incorporating metal nano-rod defects (NRDs). In the designed structures, the RRs are laterally coupled to metal–insulator-metal (MIM) waveguides. The wavelengths of the output channels depend on the numbers and radii of the metal NRDs in the RRs. The results obtained from various device geometries, with either a single or multiple output ports, are performed utilizing a single structure, showing real reconfigurability. The finite-difference time-domain (FDTD) method is used for the numerical investigation of the proposed structures. The metal and insulator used for the realization of the proposed DEMUXes are silver and air, respectively. The silver’s permittivity is characterized by the well-known Drude model. The basic plasmonic filter which is used to design plasmonic DEMUXes is a single-mode filter. A single-mode filter is easier to cope with in circuits with higher complexity such as DEMUXes. Also, different structural parameters of the basic filter are swept and their effects on the filter’s frequency response are presented, to provide a better physical insight. Taking into account the compact sizes of the proposed DEMUXes (considering the six-channel DEMUX), they can be used in integrated optical circuits for optical communication purposes.