bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Nanoceutical Agent for Chemoprevention of Bilirubin Encephalopathy
Authors: Adhikari Aniruddha , Bhutani Vinod K , Mondal Susmita , Das Monojit , Darbar Soumendra , Ghosh Ria , Polley Nabarun , Das Anjan Kumar , Bhattacharya Siddhartha Sankar , Pal Debasish , Mallick Asim Kumar , Pal Samir Kumar 
Published: January 2021

ABSTRACTBackgroundTargeted degradation of bilirubinin vivomay enable safer and more effective approach to manage incipient bilirubin encephalopathy consequent to severe neonatal hyperbilirubinemia (SNH). This report builds on the use of a spinel structured mixed-valence transition metal oxide (trimanganese tetroxide) nanoparticle duly functionalized with biocompatible ligand citrate (C-Mn3O4NP) having the ability to degrade bilirubin without photo-activation.MethodThe efficiency of C-Mn3O4NP inin vivodegradation of serum bilirubin and amelioration of severe bilirubin encephalopathy and associated neurobehavioral changes was evaluated in C57BL/6j animal model of SNH.ResultsOral single dose (0.25 mg kg-1body weight) of the NPs efficiently reduced serum bilirubin levels (both conjugated and unconjugated) in study mice. It prevents bilirubin-induced neurotoxicity with reduction of SNH as observed by neurobehavioral and movement studies of SNH-mice. Pharmacokinetic data suggests intestinal reabsorption of the NPs and explain sustainable action. Biodistribution, pharmacokinetics, and biocompatibility of the NPs were tested during sub-chronic exposure.ConclusionThus, we report preliminary studies that explore an affordable chemoprevention mechanism to acutely prevent or minimize bilirubin neurotoxicity in newborn infants.IMPACT STATEMENTDespite several attempts, no pharmaco-therapeutics are available for the treatment of severe neonatal hyperbilirubinemia (SNH) and associated neurotoxicity.Our newly developed nanodrug, citrate functionalized Mn3O4nanoparticles (C-Mn3O4NPs), can efficiently ameliorate SNH and associated neurotoxicity as investigated in preclinical rodent model.Chemoprevention effect of the nanodrug is found to be safe and sustainable.If successfully translated into clinical trials, C-Mn3O4NPs could become the first drug to treat SNH.
Identification of Discriminative Gene-level and Protein-level Features Associated with Gain-of-Function and Loss-of-Function Mutations
Authors: Bayrak Cigdem S. , Jain Aayushee , Stein David , Chaudhary Kumardeep , Nadkarni Girish N. , Van Vleck Tielman , Puel Anne , Boisson-Dupuis Stephanie , Okada Satoshi , Stenson Peter D. , Cooper David N. , Schlessinger Avner , Itan Yuval 
Published: January 2021

AbstractIdentifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms as they may result in markedly different clinical phenotypes. Here, we generated the first extensive database of all currently known germline GOF and LOF pathogenic mutations by employing natural language processing (NLP) on the available abstracts in theHuman Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF mutations by applying machine learning and statistical analyses to identify discriminative features. We found that GOF mutations were enriched in essential genes, autosomal dominant inheritance, protein binding and interaction domains, whereas LOF mutations were enriched in singleton genes, protein-truncating variants, and protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a comprehensive set of annotated features, and downloading up-to-date versions (https://itanlab.shinyapps.io/goflof/). These results could ultimately improve our understanding of how mutations affect gene/protein function thereby guiding future treatment options.
Structured illumination microscopy artifacts caused by illumination scattering
Authors: Mo Yanquan , Feng Fan , Mao Heng , Fan Junchao , Chen Liangyi 
Published: January 2021

AbstractDespite its wide application in live-cell super-resolution (SR) imaging, structured illumination microscopy (SIM) suffers from aberrations caused by various sources. Although artifacts generated from inaccurate reconstruction parameter estimation and noise amplification can be minimized, aberrations due to the scattering of excitation light on samples have rarely been investigated. In this paper, by simulating multiple subcellular structure with the distinct refractive index (RI) from water, we study how different thicknesses of this subcellular structure scatter incident light on its optical path of SIM excitation. Because aberrant interference light aggravates with the increase in sample thickness, the reconstruction of the 2D-SIM SR image degraded with the change of focus along the axial axis. Therefore, this work may guide the future development of algorithms to suppress SIM artifacts caused by scattering in thick samples.
Deciphering how naturally occurring sequence features impact the phase behaviors of disordered prion-like domains
Authors: Bremer Anne , Farag Mina , Borcherds Wade M. , Peran Ivan , Martin Erik W. , Pappu Rohit V. , Mittag Tanja 
Published: January 2021

AbstractPhase separation of intrinsically disordered prion-like low-complexity domains (PLCDs) derived from RNA-binding proteins enable the formation of biomolecular condensates in cells. PLCDs have distinct amino acid compositions, and here we decipher the physicochemical impact of conserved compositional biases on the driving forces for phase separation. We find that tyrosine residues make for stronger drivers of phase separation than phenylalanine. Depending on their sequence contexts, arginine residues enhance or weaken phase separation, whereas lysine residues weaken cohesive interactions within PLCDs. Increased net charge per residue (NCPR) weakens the driving forces for phase separation of PLCDs and this effect can be modeled quantitatively. The effects of NCPR also weaken known correlations between the dimensions of single chains in dilute solution and the driving forces for phase separation. We build on experimental data to develop a coarse-grained model for accurate simulations of phase separation that yield novel insights regarding PLCD phase behavior.
POT1 Stability and Binding Measured by Fluorescence Thermal Shift Assays
Authors: DeLeeuw Lynn W. , Monsen Robert C. , Petrauskas Vytautas , Gray Robert D. , Baranauskiene Lina , Matulis Daumantas , Trent John O. , Chaires Jonathan B. 
Published: January 2021

AbstractThe protein POT1 (Protection of Telomeres 1) is an integral part of the shelterin complex that protects the ends of human chromosomes from degradation or end fusions. It is the only component of shelterin that binds single-stranded DNA. We describe here the application of two separate fluorescent thermal shift assays (FTSA) that provide quantitative biophysical characterization of POT1 stability and its interactions. The first assay uses Sypro Orange™ and monitors the thermal stability of POT1 and its binding under a variety of conditions. This assay is useful for the quality control of POT1 preparations, for biophysical characterization of its DNA binding and, potentially, as an efficient screening tool for binding of small molecule drug candidates. The second assay uses a FRET-labeled human telomeric G-quadruplex structure that reveals the effects of POT1 binding on thermal stability from the DNA frame of reference. These complementary assays provide efficient biophysical approaches for the quantitative characterization of multiple aspects of POT1 structure and function. The results from these assays provide thermodynamics details of POT1 folding, the sequence selectivity of its DNA binding and the thermodynamic profile for its binding to its preferred DNA binding sequence. Most significantly, results from these assays elucidate two mechanisms for the inhibition of POT1 – DNA interactions. The first is by competitive inhibition at the POT1 DNA binding site. The second is indirect and is by stabilization of G-quadruplex formation within the normal POT1 single-stranded DNA sequence to prevent POT1 binding.
Viral competence data improves rodent reservoir predictions for American orthohantaviruses
Authors: Mull Nathaniel , Carlson Colin J. , Forbes Kristian M. , Becker Daniel J. 
Published: January 2021

AbstractIdentifying reservoir host species is crucial for understanding the risk of pathogen spillover from wildlife to people. Orthohantaviruses are zoonotic pathogens primarily carried by rodents that cause the diseases hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS) in humans. Given their diversity and abundance, many orthohantaviruses are expected to be undiscovered, and several host relationships remain unclear, particularly in the Americas. Despite the increasing use of predictive models for understanding zoonotic reservoirs, explicit comparisons between different evidence types for demonstrating host associations, and relevance to model performance in applied settings, have not been previously made. Using multiple machine learning methods, we identified phylogenetic patterns in and predicted unidentified reservoir hosts of New World orthohantaviruses based on evidence of infection (RT-PCR data) and competence (live virus isolation data). Infection data were driven by phylogeny, unlike competence data, and boosted regression tree (BRT) models using competence data displayed higher accuracy and a narrower list of predicted reservoirs than those using infection data. Eight species were identified by both BRT models as likely orthohantavirus hosts, with a total of 98 species identified by our infection models and 14 species identified by our competence models. Hosts predicted by competence models are concentrated in the northeastern United States (particularlyMyodes gapperiandReithrodontomys megalotis) and northern South America (several members of tribe Oryzomyini) and should be key targets for empirical monitoring. More broadly, these results demonstrate the value of infection competence data for predictive models of zoonotic pathogen hosts, which can be applied across a range of settings and host-pathogen systems.Author SummaryHuman diseases with wildlife origins constitute a significant risk for human health. Orthohantaviruses are viruses found primarily in rodents that cause disease with high rates of mortality and other complications in humans. An important step in disease prevention is to identify which rodent species carry and transmit orthohantaviruses. By incorporating species relatedness and evidence of different levels of host capacity to be infected and transmit virus, we used predictive modeling to determine unidentified rodent hosts of orthohantaviruses. Models using host competence data outperformed models using host infection data, highlighting the importance of stronger data in model optimization. Our results highlighted roughly a dozen key target species to be monitored that are concentrated in two geographic regions—northeastern United States and northern South America. More broadly, the approaches used in this study can be applied to a variety of other host-pathogen systems that threaten public health.
The histologic phenotype of lung cancers may be driven by transcriptomic features rather than genomic characteristics
Authors: Tang Ming , Abbas Hussein A , Negrao Marcelo Vailati , Ramineni Maheshwari , Hu Xin , Fujimoto Junya , Reuben Alexdrandre , Varghese Susan , Zhang Jianhua , Li Jun , Chow Chi-Wan , Mao Xizeng , Song Xingzhi , Lee Won-chul , Wu Jia , Little Latasha , Gumbs Curtis , Behrens Carmen , Moran Cesar , Weissferdt Annikka , Lee J.Jack , Sepesi Boris , Swisher Stephen , Heymach John V. , Wistuba Ignacio I. , Futreal P. Andrew , Kalhor Neda , Zhang Jianjun 
Published: January 2021

AbstractHistology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing(WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data suggest that histology of lung cancers may be determined at the transcriptomic level rather than the genomic level.
Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees
Authors: Arasumani M , Singh Aditya , Bunyan Milind , Robin V.V. 
Published: January 2021

AbstractInvasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species whereinAcacia mearnsiiandPinus patulaare highly invasive in contrast toEucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species.Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall −98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.
A conserved HSF:miR169:NF-YA loop involved in tomato and Arabidopsis heat stress tolerance
Authors: Rao Sombir , Jha Sarita , Bansal Chandni , Gupta Apoorva , Sorin Celine , Crespi Martin , Mathur Saloni 
Published: January 2021

AbstractRegulatory feedbacks are at the basis of different stress and developmental networks in plants. Here, we report that tomato and Arabidopsis plants improve their heat stress tolerance through Heat stress transcription factor (HSF)-mediated transcriptional regulation ofMIR169and post-transcriptional regulation ofNF-YAtranscription factors. We show that HSFs recognize tomato and ArabidopsisMIR169promoters using yeast-one-hybrid/ChIP-qPCR. Silencing tomato HSFs using virus-induced-gene-silencing (VIGS) reduceSly-MIR169levels and enhanceSly-NF-YA9/A10target expression. Further, tomato transgenic plants overexpressingSly-MIR169andSly-NF-YA9/A10-VIGSknock-down tomato plants as well as Arabidopsis plants overexpressingAt-MIR169dandAt-nf-ya2mutants showed a link with increased heat tolerance. In contrast, Arabidopsis plants overexpressingAt-NF-YA2,or those expressing a non-cleavableAt-NF-YA2form (miR169-resistantAt-NF-YA2) as well as plants inhibited for At-miRNA169d regulation (miR169d mimic plants) were more sensitive to heat stress, highlightingNF-YAas negative regulator of heat tolerance. Furthermore, post-transcriptional cleavage ofNF-YAby elevated miR169 levels result in alleviating the repression of heat stress effectors HSFA7a/b in tomato and Arabidopsis revealing a retroactive control of HSFs by the miR169:NF-YA node. Hence, a regulatory feedback loop involving HSFs, miR169s and NF-YAs plays a critical role in the regulation of heat stress response in tomato and Arabidopsis plants.
COVID-19 Pandemic and Behavioural Response to Self-Medication Practice in Western Uganda
Authors: Dare Samuel S. , Eze Ejike Daniel , Isaac Echoru , Usman Ibe Michael , Ssempijja Fred , Bukenya Edmund Eriya , Ssebuufu Robinson 
Published: January 2021

ABSTRACTBackgroundSelf-medication has become is a serious public health problem globally posing great risks, especially with the increasing number of cases of COVID-19 disease in Uganda. This is may be partly because of the absence of a recognized treatment for the disease, however, the prevalence and nature differ from country to country which may influence human behavioural responses.AimThis study aimed to investigated the beharioural response of the community towards self- medication practices during this COVID-19 pandemic and lockdown.MethodsA cross sectional household and online survey was conducted during the months of June-to- August. The study was conducted among adult between age 18 above in communities of western Uganda who consented to participate in the study. Study participants were selected using a convenience sampling technique and sampling was done by sending a structured online questionnaire via Google forms and a printed copies questionnaire made available to other participants that did not use the online questionnaireResultsThe percentage of respondents that know about self-medication is (97%) and those that practice self-medication are approximately (88%). 97% of respondents have heard about self-medication either through health workers, media, family members, friends and/or school while 3% said they have not heard about self-medication. The percentage of respondents who practiced self- medication during COVID-19 pandemic is 57% while those that did not is 43%. There is statistically difference in the number of those that practice self-medication and those that do not p < 0.005 at 95% confidence interval. Also there was a statistically significant decrease in the number of respondents that practice self-medication during COVID-19 pandemic lockdown compare to the practice before the pandemic lockdown p < 0.05 at 95% confidence interval.ConclusionOur investigation showed adequate knowledge of self-medication and high level of self- medication practice with a decrease in self-medication practices during the COVID-19 pandemic lockdown compared to the practice before the lockdown.
Can Catastrophe Theory explain expansion and contagious of Covid-19?
Authors: Leonel Caetano Marco Antonio 
Published: January 2021

ABSTRACTSince SARS-Cov-2 started spreading in China and turned into a pandemic disease called Covid-19, many articles about prediction with mathematical model have appeared in the literature. In addition to models in specialized journals, a significant amount of software was made available, presenting with dashboards spreading of the pandemic for each new. These models are solved by computer simulation of traditional exponential models as a representation of the growth of cases and deaths. Some more accurate models are based on existing variations of SIR model (Susceptible, Infected and Recovered). A third class of study is developed in spatial or probabilistic models as a way of forecasting the effect of Covid-19 around the world. Data on the number of positive cases in all countries, show that SARS-Cov-2 shows great resistance even after strategies of lockdown or social distancing. The purpose of this article is to show how the bifurcation theory, known as Catastrophe Theory, can help to understand why Covid-19 expansion rates change so much and even with low values for a longtime trigger contagion quickly and abruptly.The Catastrophe Theory was conceived by the mathematician René Thom in the 60s with wide applications in works in the 70s. The outbreak of spruce budworm in Canada revealed a very interesting opportunity to test Catastrophe Theory whose explanation for the phenomenon was widely debated in the academic world. Inspired by the same mathematical approach to this phenomenon in Canada in the 1970s, we applied the Catastrophe Theory in the current Covid-19 pandemic. We observed that sudden outbreaks occur when the carrying capacity and the rate of expansion of the virus reach a region of bifurcation on the cusp surface. With actual Covid-19 data obtained from WHO, we fitted the dynamic model using the particle swarm technique and compared the results in the bifurcation plan with the Covid-19 outbreaks in different regions of the world. It is possible in many cases to observe the trajectory of the parameters between limit points in the bistable region and the consequent explosion of cases observed for each country assessed.
Knowledge, attitude and practice toward COVID-19 among healthcare workers in public health facilities, Eastern Ethiopia
Authors: Farah Alinoor Mohamed , Nour Tahir Yousuf , Obsiye Muse , Adan Mowlid Aqil , Ali Omar Moeline , Hussein Muktar Arab , Budul Abdullahi Bedel , Omer Muktar , Getnet Fentabil 
Published: January 2021

AbstractOn 13 March 2020, Ethiopia reported the first confirmed case of COVID-19 in Addis Ababa. COVID-19 is likely to overwhelm an already fragile health-care delivery system and reduce the availability of services for endemic health concerns such as malaria and diarrheal diseases.Cross sectional study was conducted on heath care workers in three public health facilities in Somali region to assess knowledge, attitude and practice towards COVID-19. T-test and ANOVA were used to analyze the relationship between the dependent, and independent variables. Spearman’s correlation was used to assess the relationship between mean knowledge and attitude scores.A vast majority of the participants were male (n = 293, 67.5%), with a mean age of 27.6 (SD: 5.3) years. The mean knowledge score was 13.7 (SD: 2.6) and the mean attitude score 10.5 (SD: 4.1). Only 45.2 % (n = 196) of the participants had a good attitude toward COVID-19. There was a negative correlation between knowledge scores, attitude scores (r=-0.295, P<0.001) and practice (r=-0.298, P<0.001).The overall level of knowledge was good. However, the attitude and practice were relatively low. We recommend strategies for enhancing the capacity of healthcare workers to develop positive attitude and practice.