bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mycobacterium abscessus pathogenesis identified by phenogenomic analyses
Authors: Boeck Lucas , Burbaud Sophie , Skwark Marcin , Pearson Will H. , Sangen Jasper , Wuest Andreas W. , Marshall Eleanor K. P. , Weimann Aaron , Everall Isobel , Bryant Josephine M. , Malhotra Sony , Bannerman Bridget P. , Kierdorf Katrin , Blundell Tom L. , Dionne Marc S. , Parkhill Julian , Andres Floto R. 
Published: August 2022

AbstractThe medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome. We combined genome-wide association studies with proteome-wide computational structural modelling to define likely causal variants, and employed direct coupling analysis to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings and discover clinically relevant M. abscessus virulence factors including a secretion system, thus illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.
Evaluation of cognitive function in the Dog Aging Project: associations with baseline canine characteristics
Authors: Yarborough Sarah , Fitzpatrick Annette , Schwartz Stephen M. ,   Dog Aging Project Consortium, Creevy Kate E. , Ruple Audrey , Wilkins Vanessa , Kaeberlein Matt , Promislow Daniel , Akey Joshua M. , Benton Brooke , Borenstein Elhanan , Castelhano Marta G. , Coleman Amanda E. , Creevy Kate E. , Crowder Kyle , Dunbar Matthew D. , Fajt Virginia R. , Fitzpatrick Annette L. , Jeffery Unity , Jonlin Erica C. , Kaeberlein Matt , Karlsson Elinor K. , Kerr Kathleen F. , Levine Jonathan M. , Ma Jing , McClelland Robyn L. , Promislow Daniel E. L. , Ruple Audrey , Schwartz Stephen M. , Shrager Sandi , Snyder-Mackler Noah , Tolbert M. Katherine , Urfer Silvan R. , Wilfond Benjamin S. 
Published: August 2022

AbstractCanine cognitive dysfunction (CCD) is a neurodegenerative disease in aging dogs. It has been described previously in relatively small cohorts of dogs using multiple different rating scales. This study aimed to use a minimally modified CCD rating scale developed by previous researchers to describe the prevalence of CCD more thoroughly in a large, nationwide cohort of companion dogs participating in the Dog Aging Project (DAP) (n = 15,019). Associations between various canine characteristics, predicted lifespan quartiles, and CCD were examined using univariable and multivariable logistic regression models and receiver operating curve (ROC) analysis. When controlling for all other characteristics, the odds of CCD increased 52% with each additional year of age. Among dogs of the same age, health status, breed type, and sterilization status, odds of CCD were 6.47 times higher in dogs who were not active compared to those who were very active. When controlling for age, breed type, activity level, and other comorbidities, dogs with a history of neurological, eye, or ear disorders had higher odds of CCD. Lifespan quartile analysis showed excellent discriminating ability between CCD positive and negative dogs. Weight-based lifespan quartile estimation could therefore serve as a tool to inform CCD screening by veterinarians.
Self-reconfigurable robot vision pipeline for safer adaptation to varying pavements width and surface conditions
Authors: Yi Lim , Félix Gómez Braulio , Ramalingam Balakrishnan , Rayguru Madan Mohan , Elara Mohan Rajesh , Hayat Abdullah Aamir 
Published: August 2022

AbstractThis work presents the vision pipeline for our in-house developed autonomous reconfigurable pavement sweeping robot named Panthera. As the goal of Panthera is to be an autonomous self-reconfigurable robot, it has to understand the type of pavement it is moving in so that it can adapt smoothly to changing pavement width and perform cleaning operations more efficiently and safely. deep learning (DL) based vision pipeline is proposed for the Panthera robot to recognize pavement features, including pavement type identification, pavement surface condition prediction, and pavement width estimation. The DeepLabv3+ semantic segmentation algorithm was customized to identify the pavement type classification, an eight-layer CNN was proposed for pavement surface condition prediction. Furthermore, pavement width estimation was computed by fusing the segmented pavement region on the depth map. In the end, the fuzzy inference system was implemented by taking input as the pavement width and its conditions detected and output as the safe operational speed. The vision pipeline was trained using the DL provided with the custom pavement images dataset. The performance was evaluated using offline test and real-time field trial images captured through the reconfigurable robot Panthera stereo vision sensor. In the experimental analysis, the DL-based vision pipeline components scored 88.02% and 93.22% accuracy for pavement segmentation and pavement surface condition assessment, respectively, and took approximately 10 ms computation time to process the single image frame from the vision sensor using the onboard computer.
Lightweight convolutional neural network for aircraft small target real-time detection in Airport videos in complex scenes
Authors: Li Weidong , Liu Jia , Mei Hang 
Published: August 2022

AbstractAirport aircraft identification has essential application value in conflict early warning, anti-runway foreign body intrusion, remote command, etc. The scene video images have problems such as small aircraft targets and mutual occlusion due to the extended shooting distance. However, the detection model is generally complex in structure, and it is challenging to meet real-time detection in air traffic control. This paper proposes a real-time detection network of scene video aircraft-RPD (Realtime Planes Detection) to solve this problem. We construct the lightweight convolution backbone network RPDNet4 for feature extraction. We design a new core component CBL module(Conv (Convolution), BN (Batch Normalization), RELU (Rectified Linear Units)) to expand the range of receptive fields in the neural network. We design a lightweight channel adjustment module block by adding separable depth convolution to reduce the model’s structural parameters. The loss function of GIou loss improves the convergence speed of network training. the paper designs the four-scale prediction module and the adjacent scale feature fusion technology to fuse the adjacent features of different abstract levels. Furthermore, a feature pyramid structure with low-level to high-level is constructed to improve the accuracy of airport aircraft’s small target detection. The experimental results show that compared with YOLOv3, Faster-RCNN, and SSD models, the detection accuracy of the RPD model improved by 5.4%, 7.1%, and 23.6%; in terms of model parameters, the RPD model was reduced by 40.5%, 33.7%, and 80.2%; In terms of detection speed, YOLOv3 is 8.4 fps while RPD model reaches 13.6 fps which is 61.9% faster than YOLOv3.
Application of nickel chitosan nanoconjugate as an antifungal agent for combating Fusarium rot of wheat
Authors: Chouhan Divya , Dutta Ankita , Kumar Anoop , Mandal Palash , Choudhuri Chandrani 
Published: August 2022

AbstractAgro-researchers are endlessly trying to derive a potential biomolecule having antifungal properties in order to replace the application of synthetic fungicides on agricultural fields. Rot disease often caused by Fusarium solani made severe loss of wheat crops every year. Chitosan and its metallic nano-derivatives hold a broad-spectrum antifungal property. Our interdisciplinary study deals with the application of nickel chitosan nanoconjugate (NiCNC) against Fusarium rot of wheat, in comparison with chitosan nanoparticles (CNPs) and commercial fungicide Mancozeb. CNPs and NiCNC were characterized on the basis of UV–Vis spectrophotometry, HR-TEM, FESEM, EDXS and FT-IR. Both CNPs and NiCNC were found effective against the fungal growth, of which NiCNC at 0.04 mg/mL showed complete termination of F. solani grown in suitable medium. Ultrastructural analysis of F. solani conidia treated with NiCNC revealed pronounced damages and disruption of the membrane surface. Fluorescence microscopic study revealed generation of oxidative stress in the fungal system upon NiCNC exposure. Moreover, NiCNC showed reduction in rot disease incidence by 83.33% of wheat seedlings which was further confirmed through the observation of anatomical sections of the stem. NiCNC application helps the seedling to overcome the adverse effect of pathogen, which was evaluated through stress indices attributes.
The chronification of post-COVID condition associated with neurocognitive symptoms, functional impairment and increased healthcare utilization
Authors: Nehme Mayssam , Braillard Olivia , Chappuis François ,   CoviCare Study Team, Nehme Mayssam , Braillard Olivia , Vetter Pauline , Courvoisier Delphine S. , Assal Frederic , Lador Frederic , Benzakour Lamyae , Coen Matteo , Guerreiro Ivan , Allali Gilles , Graf Christophe , Reny Jean-Luc , Stringhini Silvia , Spechbach Hervé , Jacquerioz Frederique , Salamun Julien , Bondolfi Guido , Zekry Dina , Soccal Paola M. , Favale Riccardo , Genevay Stéphane , Lauper Kim , Meyer Philippe , Poku Nana Kwabena , Py Agathe , Landis Basile N. , Agoritsas Thomas , Grira Marwène , Sandoval José , Ehrsam Julien , Regard Simon , Genecand Camille , Tardin Aglaé , Kaiser Laurent , Chappuis François , Guessous Idris , Guessous Idris 
Published: August 2022

AbstractPost-COVID condition is prevalent in 10–35% of cases in outpatient settings, however a stratification of the duration and severity of symptoms is still lacking, adding to the complexity and heterogeneity of the definition of post-COVID condition and its oucomes. In addition, the potential impacts of a longer duration of disease are not yet clear, along with which risk factors are associated with a chronification of symptoms beyond the initial 12 weeks. In this study, follow-up was conducted at 7 and 15 months after testing at the outpatient SARS-CoV-2 testing center of the Geneva University Hospitals. The chronification of symptoms was defined as the continuous presence of symptoms at each evaluation timepoint (7 and 15 months). Adjusted estimates of healthcare utilization, treatment, functional impairment and quality of life were calculated. Logistic regression models were used to evaluate the associations between the chronification of symptoms and predictors. Overall 1383 participants were included, with a mean age of 44.3 years, standard deviation (SD) 13.4 years, 61.4% were women and 54.5% did not have any comorbidities. Out of SARS-CoV-2 positive participants (n = 767), 37.0% still had symptoms 7 months after their test of which 47.9% had a resolution of symptoms at the second follow-up (15 months after the infection), and 52.1% had persistent symptoms and were considered to have a chronification of their post-COVID condition. Individuals with a chronification of symptoms had an increased utilization of healthcare resources, more recourse to treatment, more functional impairment, and a poorer quality of life. Having several symptoms at testing and difficulty concentrating at 7 months were associated with a chronification of symptoms. COVID-19 patients develop post-COVID condition to varying degrees and duration. Individuals with a chronification of symptoms experience a long-term impact on their health status, functional capacity and quality of life, requiring a special attention, more involved care and early on identification considering the associated predictors.
Object-centered sensorimotor bias of torque control in the chronic stage following stroke
Authors: Schneider Thomas Rudolf , Hermsdörfer Joachim 
Published: August 2022

AbstractWhen lifting objects whose center of mass (CoM) are not centered below the handle one must compensate for arising external torques already at lift-off to avoid object tilt. Previous studies showed that finger force scaling during object lifting may be impaired at both hands following stroke. However, torque control in object manipulation has not yet been studied in patients with stroke. In this pilot study, thirteen patients with chronic stage left hemispheric stroke (SL), nine patients with right hemispheric stroke (SR) and hand-matched controls had to grasp and lift an object with the fingertips of their ipsilesional hand at a handle while preventing object tilt. Object CoM and therewith the external torque was varied by either relocating a covert weight or the handle. The compensatory torque at lift-off (Tcom) is the sum of the torque resulting from (1) grip force being produced at different vertical finger positions (∆CoP × GF) and (2) different vertical load forces on both sides of the handle (∆Fy × w/2). When having to rely on sensorimotor memories, ∆CoP × GF was elevated when the object CoM was on the ipsilesional-, but decreased when CoM was on the contralesional side in SL, whereas ∆Fy × w/2 was biased in the opposite direction, resulting in normal Tcom. SR patients applied a smaller ∆CoP × GF when the CoM was on the contralesional side. Torques were not altered when geometric cues were available. Our findings provide evidence for an object-centered spatial bias of manual sensorimotor torque control with the ipsilesional hand following stroke reminiscent of premotor neglect. Both intact finger force-to-position coordination and visuomotor control may compensate for the spatial sensorimotor bias in most stroke patients. Future studies will have to confirm the found bias and evaluate the association with premotor neglect.
A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools
Authors: Dutta Tithi , Mitra Sayantan , Saha Arpan , Ganguly Kausik , Pyne Tushar , Sengupta Mainak 
Published: August 2022

AbstractVitiligo is a prevalent depigmentation disorder affecting around 1% of the general population. So far, various Genome Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS) have identified several single nucleotide variants (SNVs) as a risk factor for vitiligo. Nonetheless, little has been discerned regarding their direct functional significance to the disease pathogenesis. In this study, we did extensive data mining and downstream analysis using several experimentally validated datasets like GTEx Portal and web tools like rSNPBase, RegulomeDB, HaploReg and STRING to prioritize 13 SNVs from a set of 291SNVs that have been previously reported to be associated with vitiligo. We also prioritized their underlying/target genes and tried annotating their functional contribution to vitiligo pathogenesis. Our analysis revealed genes like FGFR10P, SUOX, CDK5RAP1 and RERE that have never been implicated in vitiligo previously to have strong potentials to contribute to the disease pathogenesis. The study is the first of its kind to prioritize and functionally annotate vitiligo-associated GWAS and CGAS SNVs and their underlying/target genes, based on functional data available in the public domain database.
Pulmonary pathophysiology development of COVID-19 assessed by serial Electrical Impedance Tomography in the MaastrICCht cohort
Authors: Heines Serge J. H. , van Bussel Bas C. T. , Jong Melanie J. Acampo-de , Bennis Frank C. , van Gassel Rob J. J. , Groven Rald V. M. , Heijnen Nanon F. L. , Hermans Ben J. M. , Hounjet René , van Koll Johan , Mulder Mark M. G. , van de Poll Marcel C. G. , van Rosmalen Frank , Segers Ruud , Steyns Sander , Strauch Ulrich , Tas Jeanette , van der Horst Iwan C. C. , van Kuijk Sander M. J. , Bergmans Dennis C. J. J. 
Published: August 2022

AbstractPatients with SARS-CoV-2 infection present with different lung compliance and progression of disease differs. Measures of lung mechanics in SARS-CoV-2 patients may unravel different pathophysiologic mechanisms during mechanical ventilation. The objective of this prospective observational study is to describe whether Electrical Impedance Tomography (EIT) guided positive end-expiratory pressure (PEEP) levels unravel changes in EIT-derived parameters over time and whether the changes differ between survivors and non-survivors. Serial EIT-measurements of alveolar overdistension, collapse, and compliance change in ventilated SARS-CoV-2 patients were analysed. In 80 out of 94 patients, we took 283 EIT measurements (93 from day 1–3 after intubation, 66 from day 4–6, and 124 from day 7 and beyond). Fifty-one patients (64%) survived the ICU. At admission mean PaO2/FiO2-ratio was 184.3 (SD 61.4) vs. 151.3 (SD 54.4) mmHg, (p = 0.017) and PEEP was 11.8 (SD 2.8) cmH2O vs. 11.3 (SD 3.4) cmH2O, (p = 0.475), for ICU survivors and non-survivors. At day 1–3, compliance was ~ 55 mL/cmH2O vs. ~ 45 mL/cmH2O in survivors vs. non-survivors. The intersection of overdistension and collapse curves appeared similar at a PEEP of ~ 12–13 cmH2O. At day 4–6 compliance changed to ~ 50 mL/cmH2O vs. ~ 38 mL/cmH2O. At day 7 and beyond, compliance was ~ 38 mL/cmH2O with the intersection at a PEEP of ~ 9 cmH2O vs. ~ 25 mL/cmH2O with overdistension intersecting at collapse curves at a PEEP of ~ 7 cmH2O. Surviving SARS-CoV-2 patients show more favourable EIT-derived parameters and a higher compliance compared to non-survivors over time. This knowledge is valuable for discovering the different groups.
Whole-genome resequencing of Chinese pangolins reveals a population structure and provides insights into their conservation
Authors: Wang Qing , Lan Tianming , Li Haimeng , Sahu Sunil Kumar , Shi Minhui , Zhu Yixin , Han Lei , Yang Shangchen , Li Qian , Zhang Le , Deng Zhangwen , Liu Huan , Hua Yan 
Published: August 2022

AbstractPoaching and trafficking have a substantial negative impact on the population growth and range expansion of the Chinese pangolin (Manis pentadactyla). However, recently reported activities of Chinese pangolins in several sites of Guangdong province in China indicate a promising sign for the recovery of this threatened species. Here, we re-sequence genomes of 15 individuals and perform comprehensive population genomics analyses with previously published 22 individuals. These Chinese pangolins are found to be divided into three distinct populations. Multiple lines of evidence indicate the existence of a newly discovered population (CPA) comprises entirely of individuals from Guangdong province. The other two populations (CPB and CPC) have previously been documented. The genetic differentiation of the CPA and CPC is extremely large (FST = 0.541), which is larger than many subspecies-level differentiations. Even for the closer CPA and CPB, their differentiation (FST = 0.101) is still comparable with the population-level differentiation of many endangered species. Further analysis reveals that the CPA and CPB populations separate 2.5–4.0 thousand years ago (kya), and on the other hand, CPA and CPC diverge around 25–40 kya. The CPA population harbors more runs of homozygosity (ROHs) than the CPB and CPC populations, indicating that inbreeding is more prevalent in the CPA population. Although the CPC population has less mutational load than CPA and CPB populations, we predict that several Loss of Function (LoF) mutations will be translocated into the CPA or CPB populations by using the CPC as a donor population for genetic rescue. Our findings imply that the conservation of Chinese pangolins is challenging, and implementing genetic rescue among the three groups should be done with extreme caution.
An automated liquid jet for fluorescence dosimetry and microsecond radiolytic labeling of proteins
Authors: Rosi Matthew , Russell Brandon , Kristensen Line G. , Farquhar Erik R. , Jain Rohit , Abel Donald , Sullivan Michael , Costello Shawn M. , Dominguez-Martin Maria Agustina , Chen Yan , Marqusee Susan , Petzold Christopher J. , Kerfeld Cheryl A. , DePonte Daniel P. , Farahmand Farid , Gupta Sayan , Ralston Corie Y. 
Published: August 2022

AbstractX-ray radiolytic labeling uses broadband X-rays for in situ hydroxyl radical labeling to map protein interactions and conformation. High flux density beams are essential to overcome radical scavengers. However, conventional sample delivery environments, such as capillary flow, limit the use of a fully unattenuated focused broadband beam. An alternative is to use a liquid jet, and we have previously demonstrated that use of this form of sample delivery can increase labeling by tenfold at an unfocused X-ray source. Here we report the first use of a liquid jet for automated inline quantitative fluorescence dosage characterization and sample exposure at a high flux density microfocused synchrotron beamline. Our approach enables exposure times in single-digit microseconds while retaining a high level of side-chain labeling. This development significantly boosts the method’s overall effectiveness and efficiency, generates high-quality data, and opens up the arena for high throughput and ultrafast time-resolved in situ hydroxyl radical labeling.
LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity
Authors: Astore Courtney , Zhou Hongyi , Ilkowski Bartosz , Forness Jessica , Skolnick Jeffrey 
Published: August 2022

AbstractTo understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developedLeMeDISCO(Large-Scale Molecular Interpretation ofDiseaseComorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-basedMEDICASCYalgorithm.LeMeDISCOwas applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows thatLeMeDISCOhas much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the SABscore (68.6% vs. 8.0%). Its performance is somewhat comparable to the phenotype method-based Symptom Similarity Score, 63.7% vs. 100%, butLeMeDISCOworks for far more cases and its large comorbidity recall is attributed to shared proteins that can help provide an understanding of the molecular mechanism(s) underlying disease comorbidity. TheLeMeDISCOweb server is available for academic users at:http://sites.gatech.edu/cssb/LeMeDISCO.
An equation of state unifies diversity, productivity, abundance and biomass
Authors: Harte John , Brush Micah , Newman Erica A. , Umemura Kaito 
Published: August 2022

AbstractTo advance understanding of biodiversity and ecosystem function, ecologists seek widely applicable relationships among species diversity and other ecosystem characteristics such as species productivity, biomass, and abundance. These metrics vary widely across ecosystems and no relationship among any combination of them that is valid across habitats, taxa, and spatial scales, has heretofore been found. Here we derive such a relationship, an equation of state, among species richness, energy flow, biomass, and abundance by combining results from the Maximum Entropy Theory of Ecology and the Metabolic Theory of Ecology. It accurately captures the relationship among these state variables in 42 data sets, including vegetation and arthropod communities, that span a wide variety of spatial scales and habitats. The success of our ecological equation of state opens opportunities for estimating difficult-to-measure state variables from measurements of others, adds support for two current theories in ecology, and is a step toward unification in ecology.