bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bacterial and fungal isolation from face masks under the COVID-19 pandemic
Authors: Park Ah-Mee , Khadka Sundar , Sato Fumitaka , Omura Seiichi , Fujita Mitsugu , Hashiwaki Kazuki , Tsunoda Ikuo 
Published: July 2022

AbstractThe COVID-19 pandemic has led people to wear face masks daily in public. Although the effectiveness of face masks against viral transmission has been extensively studied, there have been few reports on potential hygiene issues due to bacteria and fungi attached to the face masks. We aimed to (1) quantify and identify the bacteria and fungi attaching to the masks, and (2) investigate whether the mask-attached microbes could be associated with the types and usage of the masks and individual lifestyles. We surveyed 109 volunteers on their mask usage and lifestyles, and cultured bacteria and fungi from either the face-side or outer-side of their masks. The bacterial colony numbers were greater on the face-side than the outer-side; the fungal colony numbers were fewer on the face-side than the outer-side. A longer mask usage significantly increased the fungal colony numbers but not the bacterial colony numbers. Although most identified microbes were non-pathogenic in humans; Staphylococcus epidermidis, Staphylococcus aureus, and Cladosporium, we found several pathogenic microbes; Bacillus cereus, Staphylococcus saprophyticus, Aspergillus, and Microsporum. We also found no associations of mask-attached microbes with the transportation methods or gargling. We propose that immunocompromised people should avoid repeated use of masks to prevent microbial infection.
Oscillations of retaining wall subject to Grob’s swelling pressure
Authors: Kozlov Maksim , Tulendinova Aizhan , Kim Jong , Ellis Grant , Skrzypacz Piotr 
Published: July 2022

AbstractThe single-degree-of-freedom nonlinear problem describing the essential dynamics of an oscillating retaining wall based on non-quaking ground and subject to Grob’s swelling pressure is considered. The periodic solutions are derived using harmonic approximation. The amplitude-frequency relation is established by employing Lambert’s special function or alternatively using linearization of the nonlinear force. Analytical results are verified using numerical simulations.
Community confounding in joint species distribution models
Authors: Van Ee Justin J. , Ivan Jacob S. , Hooten Mevin B. 
Published: July 2022

AbstractJoint species distribution models have become ubiquitous for studying species-environment relationships and dependence among species. Accounting for community structure often improves predictive power, but can also affect inference on species-environment relationships. Specifically, some parameterizations of joint species distribution models allow interspecies dependence and environmental effects to explain the same sources of variability in species distributions, a phenomenon we call community confounding. We present a method for measuring community confounding and show how to orthogonalize the environmental and random species effects in suite of joint species distribution models. In a simulation study, we show that community confounding can lead to computational difficulties and that orthogonalizing the environmental and random species effects can alleviate these difficulties. We also discuss the inferential implications of community confounding and orthogonalizing the environmental and random species effects in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the outputs from occupancy models that treat species independently or account for interspecies dependence. We illustrate how joint species distribution models that restrict the random species effects to be orthogonal to the fixed effects can have computational benefits and still recover the inference provided by an unrestricted joint species distribution model.
Contrasting reproductive strategies of two Hawaiian Montipora corals
Authors: Henley E. Michael , Quinn Mariko , Bouwmeester Jessica , Daly Jonathan , Lager Claire , Zuchowicz Nikolas , Bailey Daniel W. , Hagedorn Mary 
Published: July 2022

AbstractSessile invertebrates often engage in synchronized spawning events to increase likelihood of fertilization. Although coral reefs are well studied, the reproductive behavior of most species and the relative influence of various environmental cues that drive reproduction are not well understood. We conducted a comparative examination of the reproduction of the well-studied Hawaiian coral Montipora capitata and the relatively unknown reproduction of its congener, Montipora flabellata. Both are simultaneous hermaphroditic broadcast spawners that release egg-sperm bundles with external fertilization. Montipora capitata had a distinct reproductive pattern that resulted in coordinated gamete maturation and the synchronized release of thousands of egg-sperm bundles across two spawning pulses tightly coupled to consecutive new moon phases in June and July. Montipora flabellata exhibited a four month reproductive season with spawning that was four-fold less synchronous than M. capitata; its spawning was aperiodic with little linkage to moon phase, a broadly distributed release of only dozens or hundreds of bundles over multiple nights, and a spawning period that ranged from late June through September. The reproductive strategy of M. flabellata might prove detrimental under climate change if increased frequency and severity of bleaching events leave it sparsely populated and local stressors continue to degrade its habitat.
A strategy to quantify myofibroblast activation on a continuous spectrum
Authors: Hillsley Alexander , Santoso Matthew S. , Engels Sean M. , Halwachs Kathleen N. , Contreras Lydia M. , Rosales Adrianne M. 
Published: July 2022

AbstractMyofibroblasts are a highly secretory and contractile cell phenotype that are predominant in wound healing and fibrotic disease. Traditionally, myofibroblasts are identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers, leading to a binary classification: “activated” or “quiescent (non-activated)”. More recently, however, myofibroblast activation has been considered on a continuous spectrum, but there is no established method to quantify the position of a cell on this spectrum. To this end, we developed a strategy based on microscopy imaging and machine learning methods to quantify myofibroblast activation in vitro on a continuous scale. We first measured morphological features of over 1000 individual cardiac fibroblasts and found that these features provide sufficient information to predict activation state. We next used dimensionality reduction techniques and self-supervised machine learning to create a continuous scale of activation based on features extracted from microscopy images. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of myofibroblast activation and provide an imaging-based strategy to quantify the position of a cell on that spectrum.
Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction
Authors: Backhaus Sören J. , Aldehayat Haneen , Kowallick Johannes T. , Evertz Ruben , Lange Torben , Kutty Shelby , Bigalke Boris , Gutberlet Matthias , Hasenfuß Gerd , Thiele Holger , Stiermaier Thomas , Eitel Ingo , Schuster Andreas 
Published: July 2022

AbstractFeasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08–1.16, p < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10, p < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693, p = 0.801) and GCS (auto 0.668 vs. manual 0.686, p = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04–1.15, p < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312.
SALT: transfer learning-based threat model for attack detection in smart home
Authors: Anand Pooja , Singh Yashwant , Singh Harvinder , Alshehri Mohammad Dahman , Tanwar Sudeep 
Published: July 2022

AbstractThe next whooping revolution after the Internet is its scion, the Internet of Things (IoT), which has facilitated every entity the power to connect to the web. However, this magnifying depth of the digital pool oil the wheels for the attackers to penetrate. Thus, these threats and attacks have become a prime concern among researchers. With promising features, Machine Learning (ML) has been the solution throughout to detect these threats. But, the general ML-based solutions have been declining with the practical implementation to detect unknown threats due to changes in domains, different distributions, long training time, and lack of labelled data. To tackle the aforementioned issues, Transfer Learning (TL) has emerged as a viable solution. Motivated by the facts, this article aims to leverage TL-based strategies to get better the learning classifiers to detect known and unknown threats targeting IoT systems. TL transfers the knowledge attained while learning a task to expedite the learning of new similar tasks/problems. This article proposes a learning-based threat model for attack detection in the Smart Home environment (SALT). It uses the knowledge of known threats in the source domain (labelled data) to detect the unknown threats in the target domain (unlabelled data). The proposed scheme addresses the workable differences in feature space distribution or the ratio of attack instances to a normal one, or both. The proposed threat model would show the implying competence of ML with the TL scheme to improve the robustness of learning classifiers besides the threat variants to detect known and unknown threats. The performance analysis shows that traditional schemes underperform for unknown threat variants with accuracy dropping to 39% and recall to 56.
Role of a bacterial glycolipid in Sec-independent membrane protein insertion
Authors: Nomura Kaoru , Mori Shoko , Fujikawa Kohki , Osawa Tsukiho , Tsuda Shugo , Yoshizawa-Kumagaye Kumiko , Masuda Shun , Nishio Hideki , Yoshiya Taku , Yoda Takao , Shionyu Masafumi , Shirai Tsuyoshi , Nishiyama Ken-ichi , Shimamoto Keiko 
Published: July 2022

AbstractNon-proteinaceous components in membranes regulate membrane protein insertion cooperatively with proteinaceous translocons. An endogenous glycolipid in the Escherichia coli membrane called membrane protein integrase (MPIase) is one such component. Here, we focused on the Sec translocon-independent pathway and examined the mechanisms of MPIase-facilitated protein insertion using physicochemical techniques. We determined the membrane insertion efficiency of a small hydrophobic protein using solid-state nuclear magnetic resonance, which showed good agreement with that determined by the insertion assay using an in vitro translation system. The observed insertion efficiency was strongly correlated with membrane physicochemical properties measured using fluorescence techniques. Diacylglycerol, a trace component of E. coli membrane, reduced the acyl chain mobility in the core region and inhibited the insertion, whereas MPIase restored them. We observed the electrostatic intermolecular interactions between MPIase and the side chain of basic amino acids in the protein, suggesting that the negatively charged pyrophosphate of MPIase attracts the positively charged residues of a protein near the membrane surface, which triggers the insertion. Thus, this study demonstrated the ingenious approach of MPIase to support membrane insertion of proteins by using its unique molecular structure in various ways.
Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks
Authors: Hänsch Annika , Chlebus Grzegorz , Meine Hans , Thielke Felix , Kock Farina , Paulus Tobias , Abolmaali Nasreddin , Schenk Andrea 
Published: July 2022

AbstractAutomatic liver tumor segmentation can facilitate the planning of liver interventions. For diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can yield a higher sensitivity than contrast-enhanced CT. However, most studies on automatic liver lesion segmentation have focused on CT. In this study, we present a deep learning-based approach for liver tumor segmentation in the late hepatocellular phase of DCE-MRI, using an anisotropic 3D U-Net architecture and a multi-model training strategy. The 3D architecture improves the segmentation performance compared to a previous study using a 2D U-Net (mean Dice 0.70 vs. 0.65). A further significant improvement is achieved by a multi-model training approach (0.74), which is close to the inter-rater agreement (0.78). A qualitative expert rating of the automatically generated contours confirms the benefit of the multi-model training strategy, with 66 % of contours rated as good or very good, compared to only 43 % when performing a single training. The lesion detection performance with a mean F1-score of 0.59 is inferior to human raters (0.76). Overall, this study shows that correctly detected liver lesions in late-phase DCE-MRI data can be automatically segmented with high accuracy, but the detection, in particular of smaller lesions, can still be improved.
Air exposure moderates ocean acidification effects during embryonic development of intertidally spawning fish
Authors: Frommel Andrea Y. , Lye Sadie L. R. , Brauner Colin J. , Hunt Brian P. V. 
Published: July 2022

AbstractOcean acidification can negatively impact the early life-stages of marine fish, due to energetic costs incurred by the maintenance of acid–base homeostasis, leaving less energy available for growth and development. The embryos of intertidally spawning fishes, such as Pacific herring, are often air exposed for hours. We hypothesized that air exposure would be beneficial to the developing embryo due to a higher oxygen availability (and thus reduced metabolic costs to secure adequate oxygen) and permitting excess CO2 associated with ocean acidification to be off-gassed during emersion. To investigate this, we reared Pacific herring (Clupea pallasii) embryos under three tidal regimes (subtidal: fully immersed, low intertidal: 2 × 2 h air exposure, and high intertidal: 5 + 9 h air exposure) fully crossed with three aquatic CO2 levels (400, 1500 and 3200 µatm) at a water temperature of 9.5 °C and naturally fluctuating air temperature during air exposure. We measured the effects on embryonic development and hatch, as well as carry-over effects on larval development and survival. Air exposure during embryonic development had significant positive effects on growth, condition and survival in larval Pacific herring, with some interactive effects with CO2. Interestingly, CO2 by itself in the fully immersed treatment had no effect, but had significant interactions with air exposure. Our research suggests that air exposure during low tide can be highly beneficial to intertidally spawning fishes and needs to be taken into account in climate change studies and modeling.
Offset-decoupled deformable convolution for efficient crowd counting
Authors: Zhong Xin , Qin Jing , Guo Mingyue , Zuo Wangmeng , Lu Weigang 
Published: July 2022

AbstractCrowd counting is considered a challenging issue in computer vision. One of the most critical challenges in crowd counting is considering the impact of scale variations. Compared with other methods, better performance is achieved with CNN-based methods. However, given the limit of fixed geometric structures, the head-scale features are not completely obtained. Deformable convolution with additional offsets is widely used in the fields of image classification and pattern recognition, as it can successfully exploit the potential of spatial information. However, owing to the randomly generated parameters of offsets in network initialization, the sampling points of the deformable convolution are disorderly stacked, weakening the effectiveness of feature extraction. To handle the invalid learning of offsets and the inefficient utilization of deformable convolution, an offset-decoupled deformable convolution (ODConv) is proposed in this paper. It can completely obtain information within the effective region of sampling points, leading to better performance. In extensive experiments, average MAE of 62.3, 8.3, 91.9, and 159.3 are achieved using our method on the ShanghaiTech A, ShanghaiTech B, UCF-QNRF, and UCF_CC_50 datasets, respectively, outperforming the state-of-the-art methods and validating the effectiveness of the proposed ODConv.
Prophylactic furosemide to prevent transfusion-associated circulatory overload: a randomized controlled study in rats
Authors: Klanderman Robert B. , Bosboom Joachim J. , Veelo Denise P. , Roelofs Joris J. T. H. , de Korte Dirk , van Bruggen Robin , Vogt Liffert , van Buul Jaap D. , Hollmann Markus W. , Vroom Margreeth B. , Juffermans Nicole P. , Geerts Bart F. , Vlaar Alexander P. J. 
Published: July 2022

AbstractTransfusion-associated circulatory overload (TACO) is the leading cause of transfusion related morbidity and mortality. The only treatment is empirical use of furosemide. Our aim was to investigate if furosemide can prevent TACO. A randomized controlled trial was performed using a previously validated two-hit rat model for TACO. Volume incompliance was induced (first hit) in anemic, anesthetized Lewis rats. Rats were randomized to placebo, low-dose (5 mg kg−1) or high-dose (15 mg kg−1) furosemide-administered prior to transfusion (second-hit) and divided over two doses. Primary outcome was change in left-ventricular end-diastolic pressure (∆LVEDP) pre- compared to post-transfusion. Secondary outcomes included changes in preload, afterload, contractility and systemic vascular resistance, as well as pulmonary outcomes. Furosemide treated animals had a significantly lower ∆LVEDP compared to placebo (p = 0.041), a dose–response effect was observed. ∆LVEDP in placebo was median + 8.7 mmHg (IQR 5.9–11), + 3.9 (2.8–5.6) in the low-dose and 1.9 (− 0.6 to 5.6) in the high-dose group. The effect of furosemide became apparent after 15 min. While urine output was significantly higher in furosemide treated animals (p = 0.03), there were no significant changes in preload, afterload, contractility or systemic vascular resistance. Furosemide rapidly and dose-dependently decreases the rise in hydrostatic pulmonary pressure following transfusion, essential for preventing TACO.
Wave breaking field of relativistically intense electrostatic waves in electronegative plasma with super-thermal electrons
Authors: Mukherjee Arghya 
Published: July 2022

AbstractThe wave breaking limit of relativistically intense electrostatic waves in an unmagnetised electronegative plasma, where electrons are alleged to attach onto neutral atoms or molecules and thus forming a significant amount of negative ions, has been studied analytically. A nonlinear theory has been developed, using one-dimensional (1D) relativistic multi-fluid model in order to study the roles of super-thermal electrons, negative ion species and the Lorentz factor, on the dynamics of the wave. A generalised kappa-type distribution function has been chosen for the velocities of the electrons, to couple the densities of the fluids. By assuming the travelling wave solution, the equation of motion for the evolution of the wave in a stationary wave frame has been derived and numerical solutions have been presented. Studies have been further extended, using standard Sagdeev pseudopotential method, to discover the maximum electric field amplitude sustained by these waves. The dependence of wave breaking limit on the different input parameters such as the Lorentz factor, electron temperature, spectral index of the electron velocity distribution and on the fraction and the mass ratio of the negative to positive ion species has been shown explicitly. The wavelength of these waves has been calculated for a wide range of input parameters and its dependence on aforementioned plasma parameters have been studied in detail. These results are relevant to understand particle acceleration and relativistic wave breaking phenomena in high intensity laser plasma experiments and space environments where the secondary ion species and super-thermal electrons exist.
Length weight relationships of coleoid cephalopods from the eastern Mediterranean
Authors: Önsoy Bahadır , Salman Alp 
Published: July 2022

AbstractLength-weight relationship (LWR) studies have been widely conducted for fish. They are important because they provide information about the growth of the fish, its general wellbeing, and fitness in a marine habitat. In comparison, relatively few LWR studies have been conducted on cephalopods. A total of 13,474 specimens belonging to 28 cephalopod species was investigated to define their length–weight relationship status and Fulton’s condition factors, and compared with previous studies to evaluate life history traits and test comparability of LWR values. Isometry was found in 8 species including 2 teuthids, 2 sepiids and 4 octopods, and positive allometry was found in 2 squid species. Other species showed negative allometry. Four orders of the class Cephalopoda distributed in the Mediterranean Sea were also compared in respect of their coefficient b values, and a clear distinction was found between the orders reflecting their characteristic body types and thus lifestyles. Coefficient b values of mature animals were found lower than that of maturing ones that reflects growth of semelparous cephalopods stops or at least slows down when they reach maturity. Some extreme condition factor values were calculated for especially octopods that one of them reached to 140.91 in a deep-sea octopus Pteroctopus tetracirrhus. It suggests that there are many factors that might affect the calculations. Some of them were: different body structure and growth type in cephalopods than that of fish, different length measurement method applied in cephalopods, different body parts that might have different growth rates, and preservation methods that could affect the body shape and weight in soft bodied animals.