bluuebunny/crossref_metadata_2025_split huggingface dataset:
An efficient Planet Optimization Algorithm for solving engineering problems
Authors: Sang-To Thanh , Hoang-Le Minh , Wahab Magd Abdel , Cuong-Le Thanh 
Published: May 2022

AbstractIn this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search, are adopted for increasing accuracy and expanding searching space simultaneously. A Gauss distribution function is employed as a technique to enhance the accuracy of this algorithm. POA is evaluated using 23 well-known test functions, 38 IEEE CEC benchmark test functions (CEC 2017, CEC 2019) and three real engineering problems. The statistical results of the benchmark functions show that POA can provide very competitive and promising results. Not only does POA require a relatively short computational time for solving problems, but also it shows superior accuracy in terms of exploiting the optimum.
Identification and validation of QTLs for tuber quality related traits in greater yam (Dioscorea alata L.)
Authors: Ehounou Adou Emmanuel , Cormier Fabien , Maledon Erick , Nudol Elie , Vignes Hélène , Gravillon Marie Claire , N’guetta Assanvo Simon Pierre , Mournet Pierre , Chaïr Hâna , Kouakou Amani Michel , Arnau Gemma 
Published: May 2022

AbstractTwoDioscorea alatapopulations were generated by hand pollination between contrasted diploid genitors. Population A (74F × Kabusa) was composed of 121 progenies while population B (74F × 14M) involved 193 progenies. These two populations were assessed over two consecutive years regarding important tuber quality traits. Analysis of variance showed that the genotype had the greatest influence on the phenotypic scores. Also for some traits, effect of the year_replicate was strong. The heritabilities of most traits were high. Based on these data and a reference high-density genetic map of greater yam, a total of 34 quantitative trait loci (QTLs) were detected on 8 of the 20 yam chromosomes. They corresponded to five of each of the following traits: tuber size, shape regularity, tubercular roots, skin texture, tuber flesh oxidation, six for oxidation ratio and three for flesh colour. The fraction of total phenotypic variance attributable to a single QTL ranged from 11.1 to 43.5%. We detected significant correlations between traits and QTL colocalizations that were consistent with these correlations. A majority of QTLs (62%) were found on linkage group LG16, indicating that this chromosome could play a major role in genetic control of the investigated traits. In addition, an inversion involving this chromosome was detected in the Kabusa male. Nine QTLs were validated on a diversity panel, including three for tuber size, three for shape regularity, two for skin texture and one for tubercular roots. The approximate physical localization of validated QTLs allowed the identification of various candidates genes. The validated QTLs should be useful for breeding programs using marker-assisted selection to improve yam tuber quality.
Destructive coastal sea level oscillations generated by Typhoon Maysak in the Sea of Japan in September 2020
Authors: Medvedev Igor P. , Rabinovich Alexander B. , Šepić Jadranka 
Published: May 2022

AbstractTyphoon Maysak (Julian in the Philippines) was a powerful tropical cyclone that strongly impacted coastal regions of the Sea of Japan on 2–4 September 2020. Destructive winds, violent storm waves, and intense rainfall occurred in Japan, on the Korean Peninsula, and in Far-Eastern Russia. Devastating coastal floods caused severe damage to coastal infrastructure and to ships and boats anchored in harbours and were responsible for numerous deaths. Our study indicates that the main reason for the destructive floods was the superposition of storm surge, extreme seiches (meteorological tsunamis), and surf beats. At various sites, different types of sea level oscillations prevailed depending on the atmospheric forcing, local topographic properties, and resonant shelf/coastal zone features. The principal forcing factors of these oscillations were atmospheric pressure and wind stress, but the exact generation mechanism of each specific type of oscillation was strongly site dependent. The uniqueness of the sea level response at each site is the main challenge in our understanding of the generation process and to the mitigation of the hazardous consequences of possible future events.
Recent progress in melting heat phenomenon for bioconvection transport of nanofluid through a lubricated surface with swimming microorganisms
Authors: Alqarni Marei Saeed , Yasmin Sumeira , Waqas Hassan , Khan Shan Ali 
Published: May 2022

AbstractThe cooling of numerous microelectronic devices has become a need in today's world. Nanofluids, a novel type of heat transport fluid containing nano-sized particles embedded in a host liquid, were developed a few years ago. Impact of ultra-fine nanoparticles with oil, water, or ethylene glycol produces these fluids. Nano-liquids have a variety of applications, including engine cooling, electronic devices, biomedicine, and the manufacture of thermal exchangers. The main objective of current research article is to scrutinizes theoretically, the effects of axisymmetric magnetohydrodynamic flow of bio-convective nanoliquid through a moving surface in the occurrence of swimming microorganisms. The idea of the envisaged model is improved by considering the consequence of thermal radiation, activation energy with generalized slip effects under convective boundaries. The present analysis is developed in the form of mathematical formulation and then solved numerically. The governing flow equations are transmuted into dimensionless nonlinear ODEs system by compatible similarity transformations and then integrated this so-formulated highly nonlinear problem numerically via bvp4c built-in scheme in MATLAB. The significance of influential parameters versus velocity field, temperature profile, concentration field and motile density of microorganism’s profile are examined with the aid of graphs and tabular data. The physical interpretation of outcomes highlight that the velocity receives increment for amplified mixed convection parameter. The thermal profile is found to be reducing with a greater Prandtl number. The concentration profile of nanoparticle boosts up for greater activation energy parameter. The microorganism’s profile is reduced via bioconvection Lewis number. This investigation contains the significance of bioconvection phenomenon, thermal radiation, slip effects and activation energy under convective boundary conditions. These impacts are used in axisymmetric, stagnation point flow of bioconvective magnetized nanofluid containing swimming gyrotactic motile microorganisms over a lubricated surface. The present analysis is not yet published.
Modified Meyerhof approach for forecasting reliable ultimate capacity of the large diameter bored piles
Authors: Al-Atroush M. E. , Hefny A. M. , Sorour T. M. 
Published: May 2022

AbstractThe static loading test is undoubtedly the most reliable method for forecasting the ultimate capacity of the large diameter bored piles (LDBP). However, in-situ loading of this class of piles until reaching failure is practically seldom due to the large amount of settlement required for shaft and base mobilization. Therefore, many international design standards recommend either capacity-based or settlement-based methods to estimate the LDBP ultimate capacity in case of the impossibility of performing loading tests during the design phase. However, those methods are invariably associated with various degrees of uncertainty resulting from several factors, as evidenced in several comparative analyses available in the literature. For instance, the settlement-based method of the Egyptian code of practice (ECP 202/4) usually underestimates the ultimate capacity of LDBP. In contrast, Meyerhof's capacity-based method often overestimates the LDBP’s ultimate capacity. In this paper, a modified approach has been proposed to forecast the ultimate capacity of the LDBP. This approach was modified from Meyerhof’s classical formula (1976) through three fundamental stages. First, an assessment study was performed to evaluate the reliability of the estimated LDBP ultimate capacity using Meyerhof’s classical method. For this purpose, results of full scale loaded to failure loading LDBP test and related twenty-eight parametric numerical models with various pile geometrical and soil geotechnical parameters have been used. Based on the assessment study findings, the essential modifications were suggested in the second stage to adapt Meyerhof’s classic method. In the third stage, the results of several numerical models and in-situ loading tests were employed to assess the accuracy of the developed modified method. This study showed that Meyerhof's classical method overestimated the ultimate capacity of LDBP with an error percentage ranging from 14 to 46%. On the other side, the proposed modified approach has succeeded in estimating the ultimate capacity of loaded to failure in-situ LDBP test and twenty numerical LDBP models with error percentages ranging from 0.267 to 7.75%.
Development and validation of a nomogram to predict the risk of death within 1 year in patients with non-ischemic dilated cardiomyopathy: a retrospective cohort study
Authors: Huang Yuan , Wang Hai-Yan , Jian Wen , Yang Zhi-Jie , Gui Chun 
Published: May 2022

AbstractPredicting the chances mortality within 1 year in non-ischemic dilated cardiomyopathy patients can be very useful in clinical decision-making. This study has developed and validated a risk-prediction model for identifying factors contributing to mortality within 1 year in such patients. The predictive nomogram was constructed using a retrospective cohort study, with 615 of patients hospitalized in the First Affiliated Hospital of Guangxi Medical University between October 2012 and May 2020. A variety of factors, including presence of comorbidities, demographics, results of laboratory tests, echocardiography data, medication strategies, and instances of heart transplant or death were collected from electronic medical records and follow-up telephonic consultations. The least absolute shrinkage and selection operator and logistic regression analyses were used to identify the critical clinical factors for constructing the nomogram. Calibration, discrimination, and clinical usefulness of the predictive model were assessed using the calibration plot, C-index and decision curve analysis. Internal validation was assessed with bootstrapping validation. Among the patients from whom follow-up data were obtained, the incidence of an end event (deaths or heart transplantation within 1 year) was 171 cases per 1000 person-years (105 out of 615). The main predictors included in the nomogram were pulse pressure, red blood cell count, left ventricular end-diastolic dimension, levels of N-terminal pro b-type natriuretic peptide, medical history, in-hospital worsening heart failure, and use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers. The model showed excellent discrimination with a C-index of 0.839 (95% CI 0.799–0.879), and the calibration curve demonstrated good agreement. The C-index of internal validation was 0.826, which demonstrated that the model was quite efficacious. A decision curve analysis confirmed that our nomogram was clinically useful. In this study, we have developed a nomogram that can predict the risk of death within 1 year in patients with non-ischemic dilated cardiomyopathy. This will be useful in the early identification of patients in the terminal stages for better individualized clinical decisions.
Attosecond stable dispersion-free delay line for easy ultrafast metrology
Authors: Tyagi Akansha , Sidhu Mehra S. , Mandal Ankur , Kapoor Sanjay , Dahiya Sunil , Rost Jan M. , Pfeifer Thomas , Singh Kamal P. 
Published: May 2022

AbstractWe demonstrate a dispersion-free wavefront splitting attosecond resolved interferometric delay line for easy ultrafast metrology of broadband femtosecond pulses. Using a pair of knife-edge prisms, we symmetrically split and later recombine the two wavefronts with a few tens of attosecond resolution and stability and employ a single-pixel analysis of interference fringes with good contrast using a phone camera without any iris or nonlinear detector. Our all-reflective delay line is theoretically analyzed and experimentally validated by measuring 1st and 2nd order autocorrelations and the SHG-FROG trace of a NIR femtosecond pulse. Our setup is compact, offers attosecond stability with flexibility for independent beam-shaping of the two arms. Furthermore, we suggest that our compact and in-line setup can be employed for attosecond resolved pump-probe experiments of matter with few-cycle pulses.
Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response
Authors: D’Orazio M. , Murdocca M. , Mencattini A. , Casti P. , Filippi J. , Antonelli G. , Di Giuseppe D. , Comes M. C. , Di Natale C. , Sangiuolo F. , Martinelli E. 
Published: May 2022

AbstractHigh-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.
An image classification deep-learning algorithm for shrapnel detection from ultrasound images
Authors: Snider Eric J. , Hernandez-Torres Sofia I. , Boice Emily N. 
Published: May 2022

AbstractUltrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.
Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse
Authors: Jo Kwanhoon , Chang Dong Jin , Min Ji Won , Yoo Young-Sik , Lyu Byul , Kwon Jin Woo , Baek Jiwon 
Published: May 2022

AbstractWe sought to evaluate the performance of machine learning prediction models for identifying vision-threatening diabetic retinopathy (VTDR) in patients with type 2 diabetes mellitus using only medical data from data warehouse. This is a multicenter electronic medical records review study. Patients with type 2 diabetes screened for diabetic retinopathy and followed-up for 10 years were included from six referral hospitals sharing same electronic medical record system (n = 9,102). Patient demographics, laboratory results, visual acuities (VAs), and occurrence of VTDR were collected. Prediction models for VTDR were developed using machine learning models. F1 score, accuracy, specificity, and area under the receiver operating characteristic curve (AUC) were analyzed. Machine learning models revealed F1 score, accuracy, specificity, and AUC values of up 0.89, 0.89.0.95, and 0.96 during training. The trained models predicted the occurrence of VTDR at 10-year with F1 score, accuracy, and specificity up to 0.81, 0.70, and 0.66, respectively, on test set. Important predictors included baseline VA, duration of diabetes treatment, serum level of glycated hemoglobin and creatinine, estimated glomerular filtration rate and blood pressure. The models could predict the long-term occurrence of VTDR with fair performance. Although there might be limitation due to lack of funduscopic findings, prediction models trained using medical data can facilitate proper referral of subjects at high risk for VTDR to an ophthalmologist from primary care.
Tyrosine O-sulfation proteoforms affect HIV-1 monoclonal antibody potency
Authors: Cai Cindy X. , Doria-Rose Nicole A. , Schneck Nicole A. , Ivleva Vera B. , Tippett Brad , Shadrick William R. , O’Connell Sarah , Cooper Jonathan W. , Schneiderman Zachary , Zhang Baoshan , Gowetski Daniel B. , Blackstock Daniel , Demirji Jacob , Lin Bob C. , Gorman Jason , Liu Tracy , Li Yile , McDermott Adrian B. , Kwong Peter D. , Carlton Kevin , Gall Jason G. , Lei Q. Paula 
Published: May 2022

AbstractCAP256V2LS, a broadly neutralizing monoclonal antibody (bNAb), is being pursued as a promising drug for HIV-1 prevention. The total level of tyrosine-O-sulfation, a post-translational modification, was known to play a key role for antibody biological activity. More importantly, here wedescribe for the first time the significance of the tyrosine-O-sulfation proteoforms. We developed a hydrophobic interaction chromatography (HIC) method to separate and quantify different sulfation proteoforms, which led to the direct functionality assessment of tyrosine-sulfated species. The fully sulfated (4-SO3) proteoform demonstrated the highest in vitro relative antigen binding potency and neutralization efficiency against a panel of HIV-1 viruses. Interestingly, highly variable levels of 4-SO3were produced by different clonal CHO cell lines, which helped the bNAb process development towards production of a highly potent CAP256V2LS clinical product with high 4-SO3proteoform. This study presents powerful insight for any biotherapeutic protein development where sulfation may play an important role in product efficacy.
HIV-associated vaginal microbiome and inflammation predict spontaneous preterm birth in Zambia
Authors: Price Joan T. , Vwalika Bellington , France Michael , Ravel Jacques , Ma Bing , Mwape Humphrey , Rittenhouse Katelyn J. , De Paris Kristina , Hobbs Marcia , Nelson Julie A. , Kasaro Margaret P. , Stringer Elizabeth M. , Stringer Jeffrey S. A. 
Published: May 2022

AbstractA Lactobacillus-deficient, anaerobe-rich vaginal microbiome has been associated with local inflammation and spontaneous preterm birth (sPTB), but few studies have assessed this association in the setting of HIV. We performed metagenomic sequencing and inflammatory marker assays on vaginal swabs collected in pregnancy. We grouped samples into 7 metagenomic clusters (mgClust) using the non-redundant VIRGO catalogue, and derived inflammatory scores by factor analysis. Of 221 participants, median Shannon diversity index (SDI) was highest in HIV+ with detectable viral load (1.31, IQR: 0.85–1.66; p < 0.001) and HIV+ with undetectable virus (1.17, IQR: 0.51–1.66; p = 0.01) compared to HIV− (0.74, IQR: 0.35–1.26). Inflammatory scores positively correlated with SDI (+ 0.66, 95%CI 0.28, 1.03; p = 0.001), highest among anaerobe-rich mgClust2–mgClust6. HIV was associated with predominance of anaerobe-rich mgClust5 (17% vs. 6%; p = 0.02) and mgClust6 (27% vs. 11%; p = 0.002). Relative abundance of a novel Gardnerella metagenomic subspecies > 50% predicted sPTB (RR 2.6; 95%CI: 1.1, 6.4) and was higher in HIV+ (23% vs. 10%; p = 0.001). A novel Gardnerella metagenomic subspecies more abundant in women with HIV predicted sPTB. The risk of sPTB among women with HIV may be mediated by the vaginal microbiome and inflammation, suggesting potential targets for prevention.
Comprehensive genome analysis of Lentzea reveals repertoire of polymer-degrading enzymes and bioactive compounds with clinical relevance
Authors: Maiti Pulak Kumar , Mandal Sukhendu 
Published: May 2022

AbstractThe genus Lentzea is a rare group of actinobacteria having potential for the exploration of bioactive compounds. Despite its proven ability to produce compounds with medical relevance, Lentzea genome analysis remains unexplored. Here we show a detailed understanding of the genetic features, biosynthetic gene clusters (BGCs), and genetic clusters for carbohydrate-active enzymes present in the Lentzea genome. Our analysis determines the genes for core proteins, non-ribosomal peptide synthetase condensation domain, and polyketide synthases-ketide synthase domain. The antiSMASH-based sequence analysis identifies 692 BGCs among which 8% are identical to the BGCs that produce geosmin, citrulassin, achromosin (lassopeptide), vancosamine, anabaenopeptin NZ857/nostamide A, alkylresorcinol, BE-54017, and bezastatin. The remaining BGCs code for advanced category antimicrobials like calcium-dependent, glycosylated, terpenoids, lipopeptides, thiopeptide, lanthipeptide, lassopeptide, lingual antimicrobial peptide and lantibiotics together with antiviral, antibacterial, antifungal, antiparasitic, anticancer agents. About 28% of the BGCs, that codes for bioactive secondary metabolites, are exclusive in Lentzea and could lead to new compound discoveries. We also find 7121 genes that code for carbohydrate-degrading enzymes which could essentially convert a wide range of polymeric carbohydrates. Genome mining of such genus is very much useful to give scientific leads for experimental validation in the discovery of new-generation bioactive molecules of biotechnological importance.