Scientific Reports

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EISSN: 20452322
Published by: Springer Nature
Total articles ≅ 166,678

Latest articles in this journal

Martina Amari, Federica Alessandra Brioschi, , Federica Di Cesare, Alessandro Pecile, Alessia Giordano, Pierangelo Moretti, William Magnone, Francesco Bonato, Giuliano Ravasio
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-10; https://doi.org/10.1038/s41598-022-20408-z

Abstract:
Egyptian fruit bats have gained increasing interest being a natural reservoir for emerging zoonotic viruses. Anaesthesia is often required to allow safe handling of bats. We aimed to compare the sedative and cardiopulmonary effects of two balanced anaesthetic protocols in bats undergoing gonadectomy. Group DK (n = 10) received intramuscular dexmedetomidine (40 µg/kg) and ketamine (7 mg/kg), whereas group DBM (n = 10) received intramuscular dexmedetomidine (40 µg/kg), butorphanol (0.3 mg/kg) and midazolam (0.3 mg/kg). Induction time and cardiopulmonary parameters were recorded. If anaesthetic plan was inadequate, isoflurane was titrated-to-effect. At the end of surgery venous blood gas analysis was performed and atipamezole or atipamezole-flumazenil was administered for timed and scored recovery. In DBM group heart rate and peripheral oxygen saturation were significantly higher (p = 0.001; p = 0.003 respectively), while respiratory rate was significantly lower (p = 0.001). All bats required isoflurane supplementation with no significant differences between groups. Induction and recovery times showed no significant differences. In group DK a better recovery was scored (p = 0.034). Sodium and chloride were significantly higher in DBM group (p = 0.001; p = 0.002 respectively). Both anaesthetic protocols were comparable and can be recommended for minor procedures in bats.
, Anh Vu Le, Zhiping Lin, Zhenyu Weng, Rajesh Elara Mohan, Sathian Pookkuttath
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-14; https://doi.org/10.1038/s41598-022-19249-7

Abstract:
Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot’s performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.
Yichao Liu, Yongtan Li, Shuxiang Feng, Shufang Yan, Jinmao Wang, ,
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-16; https://doi.org/10.1038/s41598-022-20184-w

Abstract:
In this study, the chloroplast (cp) genomes of Hemiptelea davidii, Ulmus parvifolia, Ulmus lamellosa, Ulmus castaneifolia, and Ulmus pumila ‘zhonghuajinye’ were spliced, assembled and annotated using the Illumina HiSeq PE150 sequencing platform, and then compared to the cp genomes of other Ulmus and Ulmaceae species. The results indicated that the cp genomes of the five sequenced species showed a typical tetrad structure with full lengths ranging from 159,113 to 160,388 bp. The large single copy (LSC), inverted repeat (IR), and small single copy (SSC) lengths were in the range of 87,736–88,466 bp, 26,317–26,622 bp and 18,485–19,024 bp, respectively. A total of 130–131 genes were annotated, including 85–86 protein-coding genes, 37 tRNA genes and eight rRNA genes. The GC contents of the five species were similar, ranging from 35.30 to 35.62%. Besides, the GC content was different in different region and the GC content in IR region was the highest. A total of 64-133 single sequence repeat (SSR) loci were identified among all 21 Ulmaceae species. The (A)n and (T)n types of mononucleotide were highest in number, and the lengths were primarily distributed in 10–12 bp, with a clear AT preference. A branch-site model and a Bayes Empirical Bayes analysis indicated that the rps15 and rbcL had the positive selection sites. Besides, the analysis of mVISTA and sliding windows got a lot of hotspots such as trnH/psbA, rps16/trnQ, trnS/trnG, trnG/trnR and rpl32/trnL, which could be utilized as potential markers for the species identification and phylogeny reconstruction within Ulmus in the further studies. Moreover, the evolutionary tree of Ulmaceae species based on common protein genes, whole cp genome sequences and common genes in IR region of the 23 Ulmaceae species were constructed using the ML method. The results showed that these Ulmaceae species were divided into two branches, one that included Ulmus, Zelkova and Hemiptelea, among which Hemiptelea was the first to differentiate and one that included Celtis, Trema, Pteroceltis, Gironniera and Aphananthe. Besides, these variations found in this study could be used for the classification, identification and phylogenetic study of Ulmus species. Our study provided important genetic information to support further investigations into the phylogenetic development and adaptive evolution of Ulmus and Ulmaceae species.
Ellen L. Zippi, Albert K. You, Karunesh Ganguly,
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-13; https://doi.org/10.1038/s41598-022-20218-3

Abstract:
Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.
, Steffen Thiel, Søren Hansen, Maiken Lumby Henriksen, Mikkel-Ole Skjoedt, Karsten Skjodt, Zohra Hamzaei, Kirsten Madsen, Lars Lund, Edith Hummler, et al.
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-13; https://doi.org/10.1038/s41598-022-20213-8

Abstract:
Proteolytic activation of the renal epithelial sodium channel (ENaC) is increased by aldosterone. The aldosterone-sensitive protease remains unidentified. In humans, elevated circulating aldosterone is associated with increased urinary extracellular vesicle (uEVs) excretion of mannan-binding lectin associated serine protease-2 (MASP-2). We hypothesized that MASP-2 is a physiologically relevant ENaC-activating protease. It was confirmed that MASP2 mRNA is abundantly present in liver but not in human and mouse kidneys. Aldosterone-stimulation of murine cortical colleting duct (mCCD) cells did not induce MASP-2 mRNA. In human kidney collecting duct, MASP-2 protein was detected in AQP2-negative/ATP6VB1-positive intercalated cells suggestive of MASP2 protein uptake. Plasma concentration of full-length MASP-2 and the short splice variant MAp19 were not changed in a cross-over intervention study in healthy humans with low (70 mmol/day) versus high (250 mmol/day) Na+ intake despite changes in aldosterone. The ratio of MAp19/MASP-2 in plasma was significantly increased with a high Na+ diet and the ratio correlated with changes in aldosterone and fractional Na+ excretion. MASP-2 was not detected in crude urine or in uEVs. MASP2 activated an amiloride-sensitive current when co-expressed with ENaC in Xenopus oocytes, but not when added to the bath solution. In monolayers of collecting duct M1 cells, MASP2 expression did not increase amiloride-sensitive current and in HEK293 cells, MASP-2 did not affect γENaC cleavage. MASP-2 is neither expressed nor co-localized and co-regulated with ENaC in the human kidney or in urine after low Na+ intake. MASP-2 does not mediate physiological ENaC cleavage in low salt/high aldosterone settings.
Harish Chandr Chauhan, Birendra Kumar,
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-10; https://doi.org/10.1038/s41598-022-20038-5

Abstract:
Skyrmion host chiral Cu $$_2$$2 OSeO $$_3$$3 has attracted researchers due to several intriguing properties. Observation of metamagnetism in low-temperature and low-field makes the magnetic properties of Cu $$_2$$2 OSeO $$_3$$3 more complex. Here, we present an investigation on metamagnetism in Cu $$_2$$2 OSeO $$_3$$3 by analyzing its structural and magnetic properties. Study of magnetic properties reveal spin-flip of one of the Cu $$^{2+}$$2+ ions, embedded in square pyramidal CuO $$_5$$5 polyhedra, due to the development of strain in low-temperature and low-field regime. The spin-flip is found to be the main reason for field-induced first-order metamagnetic transition. Magnetic phase diagram of Cu $$_2$$2 OSeO $$_3$$3 has been constructed with the help of magnetization analyses. It is argued that the metamagnetic hysteretic field region may be low-temperature skyrmion phase with additional spiral and tilted-conical phases. A tricritical point has been observed in the phase diagram at which first-order metamagnetic hysteretic field range ceases to exist.
Hugo Soulat, Emily P. Stephen, Amanda M. Beck,
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-17; https://doi.org/10.1038/s41598-022-18475-3

Abstract:
Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.
Akshat Chulahwat, , Santiago Monedero, Francisco Jośe Diez Vizcaíno, Joaquin Ramirez, David Buckley, Adrián Cardil Forradellas
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-12; https://doi.org/10.1038/s41598-022-19875-1

Abstract:
Wildfire events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfire risk to communities focused on modeling wildfire behavior in the wildland to aid in developing fuel reduction and fire suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfire behavior that fully integrates the wildland’s characteristics and the built environment’s features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfire behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufficient attention. Predicting damage to the built environment is essential in understanding and developing fire mitigation strategies to make communities more resilient to wildfire events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfire-affected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from $$58 - 64 \%$$58-64% for the historical wildfires tested. From the results, we observe that the modified random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modified degree formulation provides better predictions for nodes with mid-range vulnerability values.
Liping Du, Huan Yang, , Ning Wei, Caixia Yu, Weitong Wang, Yun Zhao
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-13; https://doi.org/10.1038/s41598-022-20299-0

Abstract:
Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.
Yahya Jand, Mohammad Hossein Ghahremani, Amir Ghanbari, Shahram Ejtemaei-Mehr, Gilles J. Guillemin,
Published: 24 September 2022
Scientific Reports, Volume 12, pp 1-12; https://doi.org/10.1038/s41598-022-20164-0

Abstract:
Melatonin (MT), a neurohormone with immunomodulatory properties, is one of the metabolites produced in the brain from tryptophan (TRP) that has already strong links with the neuropathogenesis of Multiple sclerosis (MS). However, the exact molecular mechanisms behind that are not fully understood. There is some evidence showing that MS and MT are interconnected via different pathways: Relapses of MS has a direct correlation with a low level of MT secretion and a growing body of evidence suggest that MT be therapeutic in Experimental Autoimmune Encephalomyelitis (EAE, a recognise animal model of MS) severity. Previous studies have demonstrated that the kynurenine pathway (KP), the main pathway of TRP catabolism, plays a key role in the pathogenesis of MS in humans and in EAE. The present study aimed to investigate whether MT can improve clinical signs in the EAE model by modulating the KP. C57BL/6 mice were induced with EAE and received different doses of MT. Then the onset and severity of EAE clinical symptoms were recorded. Two biological factors, aryl hydrocarbon receptor (AhR) and NAD+ which closely interact in the KP were also assessed. The results indicated that MT treatment at all tested doses significantly decrease the EAE clinical scores and the number of demyelinating plaques. Furthermore, MT treatment reduced the mRNA expression of the KP regulatory enzyme indoleamine 2,3-dioxygenase 1(IDO-1) and other KP enzymes. We also found that MT treatment reduces the mRNA expression of the AhR and inhibits the enzyme Nicotinamide N-Methyltransferase (Nnmt) overexpression leading to an increase in NAD+ levels. Collectively, this study suggests that MT treatment may significantly attenuates the severity of EAE by altering the KP, AhR and NAD+ metabolism.
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