bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fluid mechanics of facial masks as personal protection equipment (PPE) of COVID-19 virus
Authors: Habib A. , Habib L. , Habib K. 
Published: July 2021

A fluid mechanics model of inhaled air gases, nitrogen (N2) and oxygen (O2) gases, and exhaled gas components (CO2 and water vapor particles) through a facial mask (membrane) to shield the COVID-19 virus is established. The model was developed based on several gas flux contributions that normally take place through membranes. Semiempirical solutions of the mathematical model were predicted for the N95 facial mask accounting on several parameters, such as a range of porosity size (i.e., 1–30 nm), void fraction (i.e., 10−3%–0.3%), and thickness of the membrane (i.e., 10–40 µm) in comparison to the size of the COVID-19 virus. A unitless number (Nr) was introduced for the first time to describe semiempirical solutions of O2, N2, and CO2 gases through the porous membrane. An optimum Nr of expressing the flow of the inhaled air gases, O2 and N2, through the porous membrane was determined (NO2 = NN2 = −4.4) when an N95 facial mask of specifications of a = 20 nm, l = 30 µm, and ε = 30% was used as a personal protection equipment (PPE). The concept of the optimum number Nr can be standardized not only for testing commercially available facial masks as PPEs but also for designing new masks for protecting humans from the COVID-19 virus.
Statistical analysis of ENDOR spectra
Authors: Pokern Yvo , Eltzner Benjamin , Huckemann Stephan F. , Beeken Clemens , Stubbe JoAnne , Tkach Igor , Bennati Marina , Hiller Markus 
Published: July 2021

SignificanceStatistical modeling of experimental data is gaining increasing importance in biological science due to the availability of large datasets. Here we present a statistical analysis of electron–nuclear double resonance, a technique that delivers information on the angstrom to nanometer scale around paramagnetic centers in proteins. The described method allows for recognizing experimental artifacts and provides the most probable signal as well as its uncertainty. Application to representative high-field electron–nuclear double resonance spectra of a prototype tyrosyl radical in a protein, theβ2subunit ofEscherichia coliribonucleotide reductase, demonstrates that subtle information can be uncovered, such as a distribution of molecular orientations relevant for the biological function of this essential radical.
Transparent communication about negative features of COVID-19 vaccines decreases acceptance but increases trust
Authors: Petersen Michael Bang , Bor Alexander , Jørgensen Frederik , Lindholt Marie Fly 
Published: July 2021

Significance
During a pandemic, governments face incentives to not disclose negative information about vaccines to not jeopardize public vaccine acceptance. Against these incentives, the current study provides an experimental, cross-national demonstration of the importance of transparency in communication about a vaccine against COVID-19. While disclosing negative information may increase hesitancy, transparency sustains trust in health authorities and hinders the spread of conspiracy beliefs. Accordingly, the current results provide a clear warning against succumbing to the short-term incentive of withholding information. Sustaining trust during the pandemic is critical for health authorities, both if repeated vaccinations are necessary and in preparation for future health emergencies. Among those who have already lost trust, health communication has little persuasive effect.
Cytoklepty in the plankton: A host strategy to optimize the bioenergetic machinery of endosymbiotic algae
Authors: Uwizeye Clarisse , Mars Brisbin Margaret , Gallet Benoit , Chevalier Fabien , LeKieffre Charlotte , Schieber Nicole L. , Falconet Denis , Wangpraseurt Daniel , Schertel Lukas , Stryhanyuk Hryhoriy , Musat Niculina , Mitarai Satoshi , Schwab Yannick , Finazzi Giovanni , Decelle Johan 
Published: July 2021

Significance
Symbiosis between single-celled hosts and microalgae is a widespread and ecologically important phenomenon in oceanic plankton. Investigating such relationships can increase understanding of how novel organelles are acquired. Here, we integrate multiple subcellular approaches to show how a unicellular host manipulates the bioenergetic machinery of its algal endosymbionts and maximizes organic carbon production for its own benefit. Symbionts are blocked from dividing, but their photosynthetic machinery and associated mitochondrial network dramatically expand while keeping close physical proximity to each other. Genes involved in photosynthesis and carbon fixation are up-regulated, while those for carbon storage are down-regulated. Such extreme remodeling of microalgae has not been reported in other photosymbioses and provides insight into the putative early steps of plastid acquisition.
The finiteness conjecture holds in

(SL2Z⩾0)2

*
Authors: Panti Giovanni , Sclosa Davide 
Published: July 2021

Abstract
Let A, B be matrices in

S
L

2

R

having trace greater than or equal to 2. Assume the pair A, B is coherently oriented, that is, can be conjugated to a pair having nonnegative entries. Assume also that either A, B
−1 is coherently oriented as well, or A, B have integer entries. Then the Lagarias–Wang finiteness conjecture holds for the set {A, B}, with optimal product in {A, B, AB, A
2
B, AB
2}. In particular, it holds for every pair of 2 × 2 matrices with nonnegative integer entries and determinant 1.
Complex mathematical model of the underwater robot operation
Authors: Borodavkin V A , Zazhigaev A V , Chirikov S A , Nilov A N 
Published: July 2021

Abstract
The article is devoted to a complex mathematical model of the functioning of an underwater robot that allows solving the problem of operational correction of a service task directly during its execution. The successful usage of UAV largely depends on the quality of preparation and programming of the UAV mission, including the current factors of the water environment. Since UAV works in an aquatic environment, the modeling of its functioning should be carried out taking into account both the environment itself and the dynamics of changes in its properties. The developed mathematical model was implemented in the form of a computer model and confirmed its operability within the declared ranges of initial conditions and initial data. The proposed complex mathematical model can be used for planning of assignments given available energy on board the UAV and allows to evaluate the energy for the operation of the on-board equipment after performing the UAV maneuver in the operating zone and the necessary reserves for the ascent of the unit, its detection and receiving aboard the carrier.
Classification analysis of analytical and probabilistic methods for predicting the state of complex systems
Authors: Brusnikin P M , Dudkin S O , Neretin E S 
Published: July 2021

Abstract
The paper considers a class of analytical forecasting methods, a class of probabilistic methods, and methods of artificial neural networks for solving the problem of predicting the state of complex systems on board an aircraft, such as: mathematical simulation, operator method, one-dimensional time series forecasting method, potential function method, zone method, and generalized point method.
The autonomous social robot control based on the situation analysis
Authors: Kim N V , Bodunkov N E 
Published: July 2021

Abstract
The problem of the autonomous task solution by the robotic system is considered in this paper. An approach based on the situational assessment and analysis is proposed for this. This approach makes it possible to plan the robot actions from the point of minimizing the situations informational uncertainty. The hirachical semantic descriptions of the target task, environment and the robot’s state were proposed. Within this approach the situational uncertainty in terms of informational entropy estimates. Each action, including planning, should be aimed to reduce entropy. An example of the proposed approach implementation for the object dellivery task was demonstrated. The efficiency ac the approach was shawn.
Mobile robot position assessment in the room with the use of on-board vision system
Authors: Udalova N V 
Published: July 2021

Abstract
The contemporary method of visual navigation of a mobile robot is presented. The visual navigation is an effective way to increase an accuracy of estimation for a current position of a mobile robot and its displacement. Using different specific features of a ceiling image along with photogrammetry theory permits to get a relative estimate for a current position of the mobile robot and its displacement in a room. Conventional approaches are ineffective because of presence of movable and immovable objects on the recognition scene. The ceiling image as the recognition scene is used in the proposed method. The recognition scene of ceiling is a stable and reliable image in the problem of finding reference points. Moreover there are no image distortions on the scene. All the mentioned above is a significant benefit of the proposed approach in terms of accuracy characteristics of the method of visual navigation of the mobile robot in the room.
Influence of surface damage and bulk defects on the interstrip isolation of p-type silicon strip sensors
Authors: Müller-Gosewisch J.-O. , Dierlamm A. , Nürnberg A. 
Published: July 2021

Abstract
Silicon strip sensors of upcoming tracking detectors in high
luminosity colliders usually consist of a p-doped bulk with n-type
strip implants. The general consensus is that such a design
requires an additional interstrip isolation structure such as a
p-stop implant. If there is no additional implant between the
strips, it is expected that the strip isolation will be
insufficient. Before irradiation, impurities and defects in the
material lead to positive charge in the oxide and Si/SiO_2
interface which attracts electrons from the bulk. Those electrons
accumulate just beneath the surface and between the n+ strip or
pixel implants, which decreases the interstrip resistance
significantly. Ionising radiation introduces even more charge
inside the silicon dioxide, which further decreases the interstrip
resistance. Contrary to that expectation of a decreasing interstrip
resistance due to irradiation, a high interstrip resistance was
sometimes observed after proton irradiation. Hence, bulk defects
induced by proton irradiation seem to have a non-negligible impact
on the strip isolation. Therefore, an irradiation campaign with
sensors without any interstrip isolation implant has been performed.
The use of sensors without isolation structures provides deeper
insight into the dominant isolation effects. The sensors'
performance is first evaluated before irradiation. Afterwards, a
set of sensors is irradiated with different particles in order to
systematically introduce a mixture of surface and bulk defects.
This enables the distinction and leads to a better understanding of
the different effects. Finally, a combined TCAD model is derived
which is able to describe the beneficial effect of bulk defects and
the complicated interplay between surface and bulk damage.
Changes in precipitation variability across time scales in multiple global climate model large ensembles
Authors: Wood Raul R , Lehner Flavio , Pendergrass Angeline G , Schlunegger Sarah 
Published: July 2021

Abstract
Anthropogenic changes in the variability of precipitation stand to impact both natural and human systems in profound ways. Precipitation variability encompasses not only extremes like droughts and floods, but also the spectrum of precipitation which populates the times between these extremes. Understanding the changes in precipitation variability alongside changes in mean and extreme precipitation is essential in unraveling the hydrological cycle’s response to warming. We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model. The SMILE framework allows for increased precision in estimating the evolving distribution of precipitation, allowing for forced changes in precipitation variability to be compared across climate models. We show that the scaling rates of precipitation variability, the relation between the rise in global temperature and changes in precipitation variability, are markedly robust across timescales from interannual to decadal. Over mid- and high latitudes, it is very likely that precipitation is increasing across the entire spectrum from means to extremes, as is precipitation variability across all timescales, and seasonally these changes can be amplified. Model or structural uncertainty is a prevailing uncertainty especially over the Tropics and Subtropics. We uncover that model-based estimates of historical interannual precipitation variability are sensitive to the number of ensemble members used, with ‘small’ initial-condition ensembles (of less than 30 members) systematically underestimating precipitation variability, highlighting the utility of the SMILE framework for the representation of the full precipitation distribution.
The case for digital twins in metal additive manufacturing
Authors: Gunasegaram D R , Murphy A B , Matthews M J , DebRoy T 
Published: July 2021

Abstract
The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).
Invited Commentary: Estimation and Bounds Under Data Fusion
Authors: Miao Wang , Li Wei , Hu Wenjie , Wang Ruoyu , Geng Zhi 
Published: July 2021

AbstractIn their recent article, Ogburn et al. (Am J Epidemiol. 2021;190(6):1142–1147) raised a cautionary tale for epidemiologic data fusion: Bias may occur if a variable that is completely missing in the primary data set is imputed according to a regression model estimated from an auxiliary data set. However, in some specific settings, a solution may exist. Focusing on a linear outcome regression model with a missing covariate, we show that the bias can be eliminated if the underlying imputation model for the missing covariate is nonlinear in the common variables measured in both data sets. Otherwise, we describe 2 alternative approaches existing in the data fusion literature that could partially resolve this issue: One fits the outcome model by leveraging an additional validation data set containing joint observations of the outcome and the missing covariate, and the other offers informative bounds for the outcome regression coefficients without using validation data. We justify these 3 methods in a linear outcome model and briefly discuss their extension to general settings.
Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
Authors: Janssens Rens , Zhang Xian , Kauffmann Audrey , de Weck Antoine , Durand Eric Y 
Published: July 2021

Abstract

Motivation
The identification and discovery of phenotypes from high content screening images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neural networks pretrained on non-cellular images. We introduce a novel unsupervised deep learning algorithm to cluster cellular images with similar Mode-of-Action (MOA) together using only the images’ pixel intensity values as input. It corrects for batch effect during training. Importantly, our method does not require the extraction of cell candidates and works from the entire images directly.

Results
The method achieves competitive results on the labeled subset of the BBBC021 dataset with an accuracy of 97.09% for correctly classifying the MOA by nearest neighbors matching. Importantly, we can train our approach on unannotated datasets. Therefore, our method can discover novel MOAs and annotate unlabeled compounds. The ability to train end-to-end on the full resolution images makes our method easy to apply and allows it to further distinguish treatments by their effect on proliferation.

Availability and implementation
Our code is available at https://github.com/Novartis/UMM-Discovery.

Supplementary information
Supplementary data are available at Bioinformatics online.
Evidence and implications of abnormal predictive coding in dementia
Authors: Kocagoncu Ece , Klimovich-Gray Anastasia , Hughes Laura E , Rowe James B 
Published: July 2021

AbstractThe diversity of cognitive deficits and neuropathological processes associated with dementias has encouraged divergence in pathophysiological explanations of disease. Here, we review an alternative framework that emphasizes convergent critical features of cognitive pathophysiology. Rather than the loss of ‘memory centres’ or ‘language centres’, or singular neurotransmitter systems, cognitive deficits are interpreted in terms of aberrant predictive coding in hierarchical neural networks. This builds on advances in normative accounts of brain function, specifically the Bayesian integration of beliefs and sensory evidence in which hierarchical predictions and prediction errors underlie memory, perception, speech and behaviour. We describe how analogous impairments in predictive coding in parallel neurocognitive systems can generate diverse clinical phenomena, including the characteristics of dementias. The review presents evidence from behavioural and neurophysiological studies of perception, language, memory and decision-making. The reformulation of cognitive deficits in terms of predictive coding has several advantages. It brings diverse clinical phenomena into a common framework; it aligns cognitive and movement disorders; and it makes specific predictions on cognitive physiology that support translational and experimental medicine studies. The insights into complex human cognitive disorders from the predictive coding framework may therefore also inform future therapeutic strategies.
Appearance of peanut agglutinin in the blood circulation after peanut ingestion promotes endothelial secretion of metastasis-promoting cytokines
Authors: Wang Weikun , Sindrewicz-Goral Paulina , Chen Chen , Duckworth Carrie A , Pritchard David Mark , Rhodes Jonathan M , Yu Lu-Gang 
Published: July 2021

AbstractPeanut agglutinin (PNA) is a carbohydrate-binding protein in peanuts that accounts for ~0.15% peanut weight. PNA is highly resistant to cooking and digestion and is rapidly detectable in the blood after peanut consumption. Our previous studies have shown that circulating PNA mimics the actions of endogenous galactoside-binding protein galectin-3 by interaction with tumour cell-associated MUC1 and promotes circulating tumour cell metastatic spreading. The present study shows that circulating PNA interacts with micro- as well as macro-vascular endothelial cells and induces endothelial secretion of cytokines MCP-1 (CCL2) and IL-6 in vitro and in vivo. The increased secretion of these cytokines autocrinely/paracrinely enhances the expression of endothelial cell surface adhesion molecules including integrins, VCAM and selectin, leading to increased tumour cell-endothelial adhesion and endothelial tubule formation. Binding of PNA to endothelial surface MCAM (CD146), via N-linked glycans, and subsequent activation of PI3K-AKT-PREAS40 signalling is here shown responsible for PNA-induced secretion of MCP-1 and IL-6 by vascular endothelium. Thus, in addition to its influence on promoting tumour cell spreading by interaction with tumour cell-associated MUC1, circulating PNA might also influence metastasis by enhancing the secretion of metastasis-promoting MCP-1 and IL-6 from the vascular endothelium.
CHARGE syndrome and related disorders: a mechanistic link
Authors: Ufartes Roser , Grün Regina , Salinas Gabriela , Sitte Maren , Kahl Fritz , Wong Monica T Y , van Ravenswaaij-Arts Conny M A , Pauli Silke 
Published: July 2021

Abstract
CHARGE syndrome is an autosomal dominant malformation disorder caused by pathogenic variants in the chromatin remodeler CHD7. Affected are craniofacial structures, cranial nerves and multiple organ systems. Depending on the combination of malformations present, its distinction from other congenital disorders can be challenging.
To gain a better insight into the regulatory disturbances in CHARGE syndrome, we performed RNA-Seq analysis on blood samples of 19 children with CHARGE syndrome and a confirmed disease-causing CHD7 variant in comparison with healthy control children. Our analysis revealed a distinct CHARGE syndrome pattern with downregulation of genes that are linked to disorders described to mimic the CHARGE phenotype, i.e. KMT2D and KDM6A (Kabuki syndrome), EP300 and CREBBP (Rubinstein-Taybi syndrome) and ARID1A and ARID1B (Coffin-Siris syndrome).
Furthermore, by performing protein–protein interaction studies using co-immunoprecipitation, direct yeast-two hybrid and in situ proximity ligation assays, we could demonstrate an interplay between CHD7, KMT2D, KDM6A and EP300.
In summary, our data demonstrate a mechanistic and regulatory link between the developmental disorders CHARGE-, Kabuki- and Rubinstein Taybi-syndrome providing an explanation for the overlapping phenotypes.