bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and performance of a population-based risk stratification model for COVID-19
Authors: Vela Emili , Carot-Sans Gerard , Clèries Montse , Monterde David , Acebes Xènia , Comella Adrià , Eroles Luís García , Coca Marc , Valero-Bover Damià , Piera-Jiménez Jordi , Pérez Sust Pol 
Published: May 2021

AbstractThe shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease 2019 (COVID-19). Current evidence indicates that age is far from accurate in identifying the risk of severe illness; furthermore, the count of individual risk factors has limited applicability to population-based “stratify-and-shield” strategies. We developed a COVID-19 risk stratification system that allows allocating people into four mutually-exclusive risk categories based on multivariate models for hospital admissions, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from the entire population of Catalonia (North-East Spain; 7.5 million people) and validated using an independent dataset of 218,329 individuals with PCR-confirmed COVID-19, who were infected after developing the model. This showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85 – 0.85) for hospital admissions, 0.86 (0.86 – 0.97) for ICU transfers, and 0.96 (0.96 – 0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.
Using Biological Constraints to Improve Prediction in Precision Oncology
Authors: Omar Mohamed , Dinalankara Wikum , Mulder Lotte , Coady Tendai , Zanettini Claudio , Imada Eddie Luidy , Younes Laurent , Geman Donald , Marchionni Luigi 
Published: May 2021

SummaryMany gene signatures have been developed by applying machine learning (ML) onomicsprofiles, however, their clinical utility is often hindered by limited interpretability and unstable performance in different datasets. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle invasive disease; response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers:mechanistic, by restricting the training process to features capturing a specific biological mechanism; andagnostic, in which the training didn’t use anya prioribiological information. Mechanistic models had a similar or better performance to their agnostic counterparts in the testing data, with enhanced stability, robustness, and interpretability. Our findings support the use of biological constraints to develop robust and interpretable gene signatures with high translational potential.MotivationOmics-based gene signatures often suffer from overfitting and reduced performance when tested on independent data. This usually results from the discrepancy between the high number of features compared to the much smaller number of samples used in the training process, which results in the machine learning algorithm perfectly fitting the training data with a subsequent deterioration in performance in independent cohorts. We introduce a mechanistic framework to mitigate overfitting and improve interpretability by constraining the training process to simple rank-based decision rules recapitulating relevant, cancer-related, biological mechanisms. Our approach aims at reducing the number of training variables to a pre-defined set of biologically important features in the form of gene pairs. The classification mechanism depends entirely on the relative ordering of these pairs, making it robust to data preprocessing techniques, improving the overall interpretability of the resulting models with significant translational implications. Most importantly, these pairs are configured in such a way that the decision rules resulting from the genes relative order embed and recapitulate specific biological mechanism, inherently enhancing the classifiers interpretability.
Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction
Authors: Sharifi-Noghabi Hossein , Harjandi Parsa Alamzadeh , Zolotareva Olga , Collins Colin C. , Ester Martin 
Published: May 2021

AbstractData discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid these assumptions, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients. Finally, we showed that Velodrome models generalize to different tissue types that were well-represented, under-represented, or completely absent in the training data. Overall, our results suggest that Velodrome may guide precision oncology more accurately.
Mechanical counterbalance of kinesin and dynein motors in microtubular network regulates cell mechanics, 3D architecture, and mechanosensing
Authors: Zhovmer Alexander S. , Manning Alexis , Smith Chynna , Hayes James. B. , Burnette Dylan T. , Cartagena-Rivera Alexander X. , Singh Rakesh K. , Tabdanov Erdem D. 
Published: May 2021

ABSTRACTMicrotubules (MTs) and MT motor proteins form active 3D networks made of unstretchable cables with rod-like bending mechanics that provide cells with a dynamically changing structural scaffold. In this study, we report an antagonistic mechanical balance within the dynein-kinesin microtubular motor system. Dynein activity drives microtubular network inward compaction, while isolated activity of kinesins bundles and expands MTs into giant circular bands that deform the cell cortex into discoids. Furthermore, we show that dyneins recruit MTs to sites of cell adhesion increasing topographic contact guidance of cells, while kinesins antagonize itviaretraction of MTs from sites of cell adhesion. Actin-to-microtubules translocation of septin-9 enhances kinesins-MTs interactions, outbalances activity of kinesins over dyneins and induces discoid architecture of cells. These orthogonal mechanisms of MT network reorganization highlight the existence of an intricate mechanical balance between motor activities of kinesins and dyneins that controls cell 3D architecture, mechanics, and cell-microenvironment interactions.
Single-cell RNA-sequencing reveals thoracolumbar vertebra heterogeneity and rib-genesis in pigs
Authors: Li Jianbo , Wang Ligang , Yu Dawei , Hao Junfeng , Zhang Longchao , Adeola Adeniyi C. , Mao Bingyu , Gao Yun , Wu Shifang , Zhu Chunling , Zhang Yongqing , Ren Jilong , Mu Changgai , Irwin David M. , Wang Lixian , Hai Tang , Xie Haibing , Zhang Yaping 
Published: May 2021

AbstractThoracolumbar vertebra (TLV) and rib primordium (RP) development is a common evolutionary feature across vertebrates although whole-organism analysis of TLV and RP gene expression dynamics has been lacking. Here we investigated the single-cell transcriptomic landscape of thoracic vertebra (TV), lumbar vertebra (LV), and RP cells from a pig embryo at 27 days post-fertilization (dpf) and identified six cell types with distinct gene-expression signatures. In-depth dissection of the gene-expression dynamics and RNA velocity revealed a coupled process of osteogenesis and angiogenesis during TLV and rib development. Further analysis of cell-type-specific and strand-specific expression uncovered the extremely high levels of HOXA10 3’-UTR sequence specific to osteoblast of LV cells, which may function as anti-HOXA10-antisense by counteracting the HOXA10-antisense effect to determine TLV transition. Thus, this work provides a valuable resource for understanding embryonic osteogenesis and angiogenesis underlying vertebrate TLV and RP development at the cell-type-specific resolution, which serves as a comprehensive view on the transcriptional profile of animal embryo development.
Diversity, taxonomy and evolution of archaeal viruses of the class Caudoviricetes
Authors: Liu Ying , Demina Tatiana A. , Roux Simon , Aiewsakun Pakorn , Kazlauskas Darius , Simmonds Peter , Prangishvili David , Oksanen Hanna M. , Krupovic Mart 
Published: May 2021

ABSTRACTThe archaeal tailed viruses (arTV), evolutionarily related to tailed double-stranded DNA bacteriophages of the class Caudoviricetes, represent the most common isolates infecting halophilic archaea. Only a handful of these viruses have been genomically characterized, limiting our appreciation of their ecological impacts and evolution. Here, we present 37 new genomes of haloarchaeal tailed virus isolates, more than doubling the current number of sequenced arTVs. Analysis of all 63 available complete genomes of arTVs, which we propose to classify into 14 new families, suggests ancient divergence of archaeal and bacterial tailed viruses and points to an extensive sharing of genes involved in DNA metabolism and counter defense mechanisms, illuminating common strategies of virus-host interactions with tailed bacteriophages. Coupling of the comparative genomics with the host range analysis on a broad panel of haloarchaeal species uncovered four distinct groups of viral tail fiber adhesins controlling the host range expansion. The survey of metagenomes using viral hallmark genes suggests that the global architecture of the arTV community is shaped through recurrent transfers between different biomes, including hypersaline, marine and anoxic environments.
Growth hormone transgenesis affects thermal tolerance in zebrafish (Danio rerio)
Authors: Almeida Daniela Volcan , Figueiredo Marcio Azevedo , Marins Luis Fernando 
Published: May 2021

AbstractIn fish, growth hormone (GH)-transgenesis may modify physiological mechanisms of adaptation when challenged by biotic and abiotic stressors. Thus, we evaluated whether GH overexpression can alter the thermal tolerance of adult and juvenile GH-transgenic zebrafish (Danio rerio). This study compared the thermal tolerance in non-transgenic (NT) and GH-transgenic (T) zebrafish exposed to 13 °C, 39 °C, or 28 °C (control) for 96 h. Mortality rate was checked every 12 h in juvenile (8 week-old) and adult males (6 month-old). Exposure to different temperatures revealed that GH overexpression increases the tolerance of transgenic juveniles exposed to 13 °C and diminishes the tolerance of juveniles and adults, when exposed to 39 °C. Additionally, we have analyzed transcriptional expression from the heat shock proteins (HSPs), which are mainly involved in the thermal tolerance mechanism. The mRNA level analysis results revealed that, under controlled conditions (28 °C), GH-transgenesis upregulates the expression of hsp47, hsp70, hsp90a and heat shock transcription factor (hsf1a) in transgenic juveniles, although the same result was not observed in transgenic adults. Exposure to low temperature did not alter the expression of any analyzed gene, both in adults and in juveniles. Exposure to 39 °C decreased the expression of all genes analyzed, in GH-transgenic adults. Furthermore, the HSP expression pattern was analyzed via hierarchical clustering. This analysis revealed two major clusters illustrating the dependency of gene changes related to age. These results indicate that the GH overexpression can alter thermal tolerance of fish, depending of age and temperature.Highlights-GH-transgenesis increased the survival rate of juveniles at low temperature;-High temperature is more lethal for juvenile ande adult GH-transgenic zebrafish;-GH-transgenesis increased expression of hsf1a, hsp47, and hsp70 genes in juvenile zebrafish;-hsf1a, hsp47, hsp70, hsp90a, and hsp90b genes expression is diminished in adult zebrafish GH-transgenesis exposure at high temperature.Abstract Figure
Large protein complex interfaces have evolved to promote cotranslational assembly
Authors: Badonyi Mihaly , Marsh Joseph A 
Published: May 2021

AbstractAssembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromers in bacterial, yeast, and human complexes. We detect a clear preference in N-terminal interfaces, which will be translated first and thus more likely to form cotranslationally, to be larger than C-terminal interfaces, suggesting that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.
IRIXS Spectrograph: an ultra high-resolution spectrometer for tender RIXS
Authors: Bertinshaw Joel , Mayer Simon , Dill Frank-Uwe , Suzuki Hakuto , Leupold Olaf , Jafari Atefeh , Sergueev Ilya , Spiwek Manfred , Said Ayman , Kasman Elina , Huang Xianrong , Keimer Bernhard , Gretarsson Hlynur 
Published: May 2021

The IRIXS Spectrograph represents a new design of an ultra-high-resolution resonant inelastic X-ray scattering (RIXS) spectrometer that operates at the Ru L
3-edge (2840 eV). First proposed in the field of hard X-rays by Shvyd'ko [(2015), Phys. Rev. A, 91, 053817], the X-ray spectrograph uses a combination of laterally graded multilayer mirrors and collimating/dispersing Ge(111) crystals optics in a novel spectral imaging approach to overcome the energy resolution limitation of a traditional Rowland-type spectrometer [Gretarsson et al. (2020), J. Synchrotron Rad.
27, 538–544]. In combination with a dispersionless nested four-bounce high-resolution monochromator design that utilizes Si(111) and Al2O3(110) crystals, an overall energy resolution better than 35 meV full width at half-maximum has been achieved at the Ru L
3-edge, in excellent agreement with ray-tracing simulations.
Flow control method by applying flexible shells procedure in transonic flow
Authors: Saber M.R. , Djavareshkian M.H. 
Published: May 2021

Purpose
In the present research, the effect of the flexible shells method in unsteady viscous flow around airfoil has been studied. In the presented algorithm, due to the interaction of the aerodynamic forces and the structural stiffness (fluid-structural interaction), a geometrical deformation as the bump is created in the area where the shock occurs. This bump causes instead of compressive waves, a series of expansion waves that produce less drag and also improve the aerodynamic performance to be formed. The purpose of this paper is to reduce wave drag throughout the flight range. By using this method, we can be more effective than recent methods throughout the flight because if there is a shock, a bump will form in that area, and if the shock does not occur, the shape of the airfoil will not change.

Design/methodology/approach
In this simulation pressure-based procedure to solve the Navier-Stokes equation with collocated finite volume formulation has been developed. For this purpose, a high-resolution scheme for fluid and structure simulation in transonic flows with an arbitrary Lagrangian-Eulerian method is considered. To simulate Navier-Stokes equations large eddy simulation model for compressible flow is used.

Findings
A new concept has been defined to reduce the transonic flow drag. To reduce drag force and increase the performance of airfoil in transonic flow, the shell can be considered flexible in the area of shock on the airfoil surface. This method refers to the use of smart materials in the aircraft wing shell.

Originality/value
The value of the paper is to develop a new approach to improve the aerodynamic performance and reduce drag force and the efficiency of the method throughout the flight. It is noticeable that the new algorithm can detect the shock region automatically; this point was disregarded in the previous studies. It is hoped that this research will open a door to significantly enhance transonic airfoil performance.
Industry 4.0 disruption: assessing the need for adaptive capability
Authors: Rangaswamy Uma Shankar 
Published: May 2021

Purpose
Considering the advent of Industry 4.0, organizations need to undergo digital transformation to survive disruptions owing to rapid technological changes. Accordingly, organizations should develop their adaptive capability to respond to these technological advancements and move ahead with their digital transformation journey.

Design/methodology/approach
Based on the author’s insights as a practitioner on digital transformation and based on existing theoretical frameworks, the author presents a conceptual framework on the determinants of adaptive capability required for digital transformation.

Findings
Organizations should develop their adaptive capability through improved learning capabilities and effective knowledge management system. They should also focus on exploring innovations and ideas and discard unwanted business processes to meet Industry 4.0 disruptions and achieve digital transformation.

Originality/value
This study has been done based on the author's academic research on adaptive capability and personal insights after engaging in several digital transformation engagements.
State, institutional entrepreneurship and the creation of the biopharmaceutical industry in a developing country
Authors: Babaee Ali , Mashayekhi Ali N. , HamidiMotlagh Rouholah 
Published: May 2021

Purpose
This study aims to explore the emergence and development of new industries, especially in the context of developing countries, by considering industry emergence and development as a large-scale institutional change or transition.

Design/methodology/approach
The authors conducted an inductive case study research on the emergence of the biopharmaceutical industry in a developing country. The data on the emergence and development of Iran’s biopharmaceutical industry during 1990 and 2018 were collected through semi-structured interviews, participation in meetings, visits to companies and analysis of archival texts. The data analysis was an inductive and iterative process.

Findings
In the emergence and development of the biopharmaceutical industry, there have been a few key agents, institutional entrepreneurs (IEs), in both the state and private sectors, who played main roles. Moreover, the most important type of knowledge which has been crucial for the decision-making of IEs has been informal, tacit and institutional knowledge. Furthermore, the authors identified a mechanism, inter-institutional circulation, as being most effective in the transfer of institutional knowledge among IEs.

Originality/value
The originality of this study is applying insights from institutional entrepreneurship and knowledge management literature to the extant literature on industry emergence and development.
Effects of process parameters on compressive property of FDM with ABS
Authors: Nathaphan Supphachai , Trutassanawin Worrasid 
Published: May 2021

Purpose
This work aims to investigate the interaction effects of printing process parameters of acrylonitrile butadiene styrene (ABS) parts fabricated by fused deposition modeling (FDM) technology on both the dimensional accuracy and the compressive yield stress. Another purpose is to determine the optimum process parameters to achieve the maximum compressive yield stress and dimensional accuracy at the same time.

Design/methodology/approach
The standard cylindrical specimens which produced from ABS by using an FDM 3D printer were measured dimensions and tested compressive yield stresses. The effects of six process parameters on the dimensional accuracy and compressive yield stress were investigated by separating the printing orientations into horizontal and vertical orientations before controlling five factors: nozzle temperature, bed temperature, number of shells, layer height and printing speed. After that, the optimum process parameters were determined to accomplish the maximum compressive yield stress and dimensional accuracy simultaneously.

Findings
The maximum compressive properties were achieved when layer height, printing speed and number of shells were maintained at the lowest possible values. The bed temperature should be maintained 109°C and 120°C above the glass transition temperature for horizontal and vertical orientations, respectively.

Practical implications
The optimum process parameters should result in better FDM parts with the higher dimensional accuracy and compressive yield stress, as well as minimal post-processing and finishing techniques.

Originality/value
The important process parameters were prioritized as follows: printing orientation, layer height, printing speed, nozzle temperature and bed temperature. However, the number of shells was insignificant to the compressive property and dimensional accuracy. Nozzle temperature, bed temperature and number of shells were three significant process parameters effects on the dimensional accuracy, while layer height, printing speed and nozzle temperature were three important process parameters influencing compressive yield stress. The specimen fabricated in horizontal orientation supported higher compressive yield stress with wide processing ranges of nozzle and bed temperatures comparing to the vertical orientation with limited ranges.