bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transmission and Transit in Contemporary Arabic Literature: Naql and Its Limits
Authors: Paul Drew 
Published: March 2022

Abstract
In this essay, I use the concept of naql to connect textual transmission and physical movement in two contemporary Arabic fictional works that are structured around questions of the circulation of narratives and bodies: Khālid al-Khamīsī’s Tāksī, a fictional collection of tales about Cairo taxi drivers, and Aḥmad Saʿdāwī’s Frānkishtāyn fī Baghdād, which depicts a monster roaming the streets of Baghdad. I begin with a discussion of naql as both transmission and movement, reading a tension between fixity and change that complicates notions of authenticity and authority in realms such as language, time, and mobility. I then argue that Tāksī uses the mobility of the taxi and the types of transmissions that it receives and produces to reposition cultural and political knowledge gleaned from cabdrivers who move through the gridlocked streets of Cairo. In Saʿdāwī’s novel, the monster’s movements and actions begin as a response to violence but fail to cohere as an alternative to structural corruption, revealing the limits of naql as political critique. I conclude by considering how this reading of naql is reflected in the global circulation of these texts.
Applying History in Real Time
Authors: Ferguson Niall 
Published: March 2022

Abstract
Policymakers generally have at least some historical assumptions, even if they have not been trained as historians. Often, these assumptions play a critical part in decision-making, especially in times of crisis. This article compares the reactions of George W. Bush’s administration to two epoch-making events: the 9/11 terrorist attacks of 2001 and the “9/15” bankruptcy of Lehman Brothers in 2008. I argue that historical analogies played a key role in the debates between officials about how to respond to the two crises. However, the two sets of decision-makers inhabited different conceptual worlds. Those concerned with national security in 2001 thought differently about risk from those concerned with financial stability in 2008. This meant that they applied history to the crises they confronted with differing degrees of success.
Theory of In-Cloud Activation of Aerosols and Microphysical Quasi-Equilibrium in a Deep Updraft
Authors: Phillips Vaughan T. J. 
Published: March 2022

Abstract
The microphysical quasi-equilibrium in an ascending adiabatic parcel is elucidated by an analytical theory in 0D with drastic simplifications. The theory predicts how in-cloud activation is most likely to be triggered by the onset of precipitation during sufficient ascent, with the ascent only needing to approach almost twice the cloud-base updraft speed aloft. The precipitation and cloud condensate mass fields are coupled in a closed system in a simplified version of the model. The initial state of no precipitation is unstable with respect to a perturbation. In the 2D phase space of both mass fields, there is a neutral line. Unstable growth of precipitation mass drives the system to cross the line into a regime of stability. An attractor is then approached consisting of precipitation mass balanced by accretion of cloud mass and fallout. The cloud-particle number concentration also approaches a stable equilibrium governed by the balance between in-cloud activation aloft from the inexorably increasing supersaturation during vertical acceleration and accretion of cloud droplets by precipitation. Dimensionless numbers characterizing the microphysical equilibria and their stability are derived mathematically, including a condensation–precipitation efficiency and an in-cloud activation efficiency. The theory explains common observations of the orders of magnitude of liquid water content in convective and stratiform clouds. Finally, sensitivity tests of the numerically integrated theoretical equations are documented with respect to variations in cloud condensation nucleus (CCN) aerosols and updraft speed. It is shown that this theory of in-cloud activation, with an increasing supersaturation during ascent, applies to both ice-only and liquid-only cloud.

Significance Statement
The purpose of the study is to create a theory for how equilibria among cloud-microphysical properties are attained during ascent. Continual initiation of fresh cloud droplets far above cloud base in the free troposphere is explained. The theory proposes a balance between creation and depletion of such droplets, in relation to a balance between creation and fallout of precipitation. The transition from positive feedbacks of instability toward negative feedbacks of stability when the equilibria are approached is elucidated. The theory explains why the mass content of cloud liquid in regions of ascent is typically about an order of magnitude lower in layer clouds with weak ascent than in convective clouds with faster ascent. Any future work could extend the theory to treat dilute parcels or coexistence of supercooled liquid and ice.
Contribution of Thunderstorms to Changes in Hourly Extreme Precipitation over China from 1980 to 2011
Authors: Ng Chan-Pang , Zhang Qinghong , Li Wenhong , Zhou Ziwei 
Published: March 2022

Abstract
In many countries, thunderstorms are the main contributor to hourly extreme precipitation (HEP). Prior studies have shown that the number of thunderstorms decreased steadily in whole country of China; however, HEP has increased significantly in several areas over the past half-century. The role of thunderstorms in changes in HEP occurrence remains largely unknown in China. In this study, for the first time, we used continuous 32-yr records of hourly precipitation and thunder, and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), to analyze changes in thunderstorms under various vertical wind shear (VWS) environments, and their contribution to HEP occurrence. The number of HEP events associated with thunderstorms (TD-HEP) increased significantly in southern China (SC) but decreased significantly in northeastern China (NEC) and east of the Tibetan Plateau (ETP). Weak VWS thunderstorms accounted for 69.1% of TD-HEP in SC. Changes in the most unstable convective available potential energy and precipitable water (PW) in SC favored an increase in weak-VWS thunderstorms, which resulted in an increase of 2.35 h per warm season in overall “station-mean” TD-HEP events from 1980 to 2011. As the major contributor to HEP in NEC, moderate VWS thunderstorms decreased by 0.37 h per warm season, due mainly to a reduction in PW, leading to a negative trend in TD-HEP events. Similarly, the decreasing TD-HEP occurrence on the ETP was due to a decrease of 1.12 h per warm season of moderate VWS thunderstorms. Studying the VWS environments of thunderstorms, and changes therein under a warming climate, can improve understanding of the changes in HEP in China.
High Drag States in Tidally Modulated Stratified Wakes
Authors: Puthan Pranav , Pawlak Geno , Sarkar Sutanu 
Published: March 2022

Abstract
Large-eddy simulations (LES) are employed to investigate the role of time-varying currents on the form drag and vortex dynamics of submerged 3D topography in a stratified rotating environment. The current is of the form Uc + Utsin(2πftt), where Uc is the mean, Ut is the tidal component, and ft is its frequency. A conical obstacle is considered in the regime of low Froude number. When tides are absent, eddies are shed at the natural shedding frequency fs,c. The relative frequency
is varied in a parametric study, which reveals states of high time-averaged form drag coefficient. There is a twofold amplification of the form drag coefficient relative to the no-tide (Ut = 0) case when
lies between 0.5 and 1. The spatial organization of the near-wake vortices in the high drag states is different from a Kármán vortex street. For instance, the vortex shedding from the obstacle is symmetric when
and strongly asymmetric when
. The increase in form drag with increasing
stems from bottom intensification of the pressure in the obstacle lee which we link to changes in flow separation and near-wake vortices.
Comparison of Visual Features for Image-Based Visibility Detection
Authors: Tang Rong , Li Qian , Tang Shaoen 
Published: March 2022

Abstract
The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection.

Significance Statement
The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions.
Diagnosing Supercell Environments: A Machine Learning Approach
Authors: Shield Stephen A. , Houston Adam L. 
Published: March 2022

Abstract
The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting.

Significance Statement
Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms.
Quantitative assessment of the intraorbital segment of the optic nerve in patients with thyroid orbitopathy using diffusion tensor imaging
Authors: Li Rui , Li Jing , Wang Zhenchang 
Published: March 2022

Background It is difficult to detect optic nerve changes in patients with thyroid-associated ophthalmopathy (TAO) before the development of optic neuropathy. Purpose To detect changes in the intraorbital segment of the optic nerve in patients with TAO using diffusion tensor imaging (DTI) and determine any correlations with disease severity. Material and Methods A total of 74 participants (17 normal, 22 mild, and 35 moderate-severe TAO) were organized to be given 3-T DTI to measure fractional anisotropy (FA) and mean diffusivity (MD) for the orbital segments of their optic nerves. All underwent ophthalmological examinations for visual acuity, intraocular pressure, exophthalmos, and fundoscopy, and were assessed based on the Clinical Activity Score (CAS). Univariate analysis of variance and Pearson's correlation coefficients were carried out. Results Patients with moderate-to-severe TAO had significantly lower FA values and higher MD values ( P < 0.05). FA values were negatively correlated with CAS but had no obvious correlations with the degree of exophthalmos. MD values had no obvious correlations with CAS or the degree of exophthalmos. Conclusion DTI parameters of the intraorbital optic nerves were significantly altered in moderate-to-severe patients before onset of clinical optic nerve dysfunction and may, therefore, be used as an imaging biomarker for assessing the disease.
Handwashing Practices and Its Predictors Among Primary School Children in Damote Woide District, South Ethiopia: An Institution Based Cross-Sectional Study
Authors: Admasie Amha , Guluma Alemu , Feleke Fentaw Wassie 
Published: March 2022

Background: Handwashing in schools with soap could substantially reduce diarrhea and respiratory infections among school-age children; however, in low-and-middle-income countries, handwashing is still being practiced to a very low extent in particular critical moments such as before eating and after using the toilet. Therefore, the main objective of this study was to assess the level of handwashing practice and its predictors among primary school children in South Ethiopia. Methods: A school-based cross-sectional study was conducted using a multistage cluster sampling technique from 6 primary schools with 580 students in total. Schools were purposively selected and the students were random. Data were collected using pre-tested questionnaires administered by interviewers and trained data collectors. Data were entered using Epi Data and exported to SPSS software for analysis. Both bivariate and multivariable logistic regression analyzes were used. Result: Proper handwashing practice was reported in 28.10% (95% CI, 24.5, 31.7%) of students. Being eighth grade (AOR = 3.44, 95% CI 1.52, 8.23), urban residence (AOR = 18.84, 95% CI 14.02, 23.29], having parents (AOR = 10.74; 95% CI 8.80-12.36), role model teachers (AOR = 6.45; 95% CI 5.52-8.99), role model health professionals (AOR = 9.62; 95% CI 2.70-14.19), and school handwashing facility (AOR = 3.84, 95% CI 3.60, 4.07) were predictors of proper handwashing practice. Conclusions: Proper handwashing practice among schoolchildren was found below. Therefore, promoting and improving handwashing practices and preparing handwashing facilities in schools is mandatory to address the handwashing practice gap among primary school students in the study area.
Screening for phenotypic outliers identifies an unusually low concentration of a β-lactoglobulin B protein isoform in bovine milk caused by a synonymous SNP
Authors: Davis Stephen R. , Ward Hamish E. , Kelly Van , Palmer David , Ankersmit-Udy Alexandra E. , Lopdell Thomas J. , Berry Sarah D. , Littlejohn Mathew D. , Tiplady Kathryn , Adams Linda F. , Carnie Katie , Burrett Alayna , Thomas Natalie , Snell Russell G. , Spelman Richard J. , Lehnert Klaus 
Published: March 2022

Abstract
Background
Milk samples from 10,641 dairy cattle were screened by a mass spectrometry method for extreme concentrations of the A or B isoforms of the whey protein, β-lactoglobulin (BLG), to identify causative genetic variation driving changes in BLG concentration.

Results
A cohort of cows, from a single sire family, was identified that produced milk containing a low concentration of the BLG B protein isoform. A genome-wide association study (GWAS) of BLG B protein isoform concentration in milk from AB heterozygous cows, detected a group of highly significant single nucleotide polymorphisms (SNPs) within or close to the BLG gene. Among these was a synonymous G/A variation at position + 78 bp in exon 1 of the BLG gene (chr11:103256256G > A). The effect of the A allele of this SNP (which we named B’) on BLG expression was evaluated in a luciferase reporter assay in transfected CHO-K1 and MCF-7 cells. In both cell types, the presence of the B’ allele in a plasmid containing the bovine BLG gene from -922 to + 898 bp (relative to the transcription initiation site) resulted in a 60% relative reduction in mRNA expression, compared to the plasmid containing the wild-type B sequence allele. Examination of a mammary RNAseq dataset (n = 391) identified 14 heterozygous carriers of the B’ allele which were homozygous for the BLG B protein isoform (BB’). The level of expression of the BLG B’ allele was 41.9 ± 1.0% of that of the wild-type BLG B allele. Milk samples from three cows, homozygous for the A allele at chr11:103,256,256 (B’B’), were analysed (HPLC) and showed BLG concentrations of 1.04, 1.26 and 1.83 g/L relative to a mean of 4.84 g/L in milk from 16 herd contemporaries of mixed (A and B) BLG genotypes. The mechanism by which B’ downregulates milk BLG concentration remains to be determined.

Conclusions
High-throughput screening and identification of outliers, enabled the discovery of a synonymous G > A mutation in exon 1 of the B allele of the BLG gene (B’), which reduced the milk concentration of β-lactoglobulin B protein isoform, by more than 50%. Milk from cows carrying the B’ allele is expected to have improved processing characteristics, particularly for cheese-making.
A-Prot: protein structure modeling using MSA transformer
Authors: Hong Yiyu , Lee Juyong , Ko Junsu 
Published: March 2022

Abstract
Background
The accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary.

Results
In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14.

Conclusion
These results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.
GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences
Authors: Huang Yu-An , Huang Zhi-An , Li Jian-Qiang , You Zhu-Hong , Wang Lei , Yi Hai-Cheng , Yu Chang-Qing 
Published: March 2022

AbstractBackgroundRecent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological “haystack” merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale.ResultsBased on the known microbe-disease associations derived from theHuman Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures.ConclusionBased on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.