bluuebunny/crossref_metadata_2025 huggingface dataset:
Fatigue study of an inverse-designed low induction rotor using open-source tools
Authors: Major D , Churchfield M , Schmitz S
Published: June 2022

The current trend in wind turbine design leans toward increasing rotor diameter for increased energy capture. This presents a notable fatigue concern as blade loads increase with blade length. One solution to potentially alleviate the additional blade loading and improve the fatigue behavior of large-diameter wind turbines, while also reducing overall cost of energy, is the low-induction rotor. The purpose of this work is to perform a holistic analysis to assess the attainable benefits of a notional low-induction rotor variant of the DTU 10MW Reference Wind Turbine using multi-disciplinary open-source design and analysis tools. The notional low-induction rotor blade is designed using an inverse-design routine that allows for the direct specification of desired blade properties. To assess viability of the new rotor design, annual energy production and levelized cost of energy are considered along with fatigue at the blade root and the associated drivetrain and tower response to changes in blade loading. It is estimated that the proposed early-concept low-induction rotor design improves annual energy production by 6.35% (or 3 GW-h), resulting in a 4% (0.25 ¢/kW-h) reduction in levelized cost of energy. The low-thrust nature of the low-induction rotor design reduces root flap bending moment fatigue by up to 21.5%, and it is proposed that changes in blade root edgewise fatigue for the longer, heavier blades of the low-induction rotor can be alleviated with mass redistribution.
Assessing the economical impact of innovations for offshore wind farms through a holistic modelling approach
Authors: Sumetha-Aksorn Potchara , Dykes Katherine , Ceyhan Yilmaz Ozlem
Published: June 2022

In this work, a holistic modelling approach is developed and applied to the valuation of innovations in offshore wind energy. Two innovations are considered: 1) a more accurate modelling of vessel movement related to operations and maintenance activities and 2) an operational strategy for market participation. In both cases, a standard process was followed of first mapping the effects of the technologies on wind energy system’s levels, then assessing them through development and augmentation of a holistic cost and valuation model. The process is demonstrated for each innovation and quantifies their potential impacts to LCoE and NPV respectively. The models and methods can be extended to other innovations for quantitative assessment of potential benefits.
Development of engineering cost models for integrated design optimization of onshore and offshore wind farms
Authors: Yilmazlar Kutay , Cacciola Stefano , Xiolesmy Alfonso Rodriguez Maryi , Croce Alessandro
Published: June 2022

The goal of any multidisciplinary design optimization problem for wind power plants is to reduce the overall levelized cost of the energy. When it comes to designing a wind farm, one has to find the best combination of multiple parameters, such as turbine types, turbine dimensions and farm layout, to ensure the minimum cost. Clearly, since any design parameter may affect several cost items and performance indices of a farm, the most cost-effective solution should handle the mutual coupling among all design variables. For example, increasing the turbine spacing surely has a positive impact on energy production due to the minimization of wake losses, but, at the same time, may have a detrimental impact on the cost of cabling. In order to assist multidisciplinary design activities for wind farms and wind turbines, the present work is aimed at developing a tool for preliminary estimation of the levelized cost of energy of land- and sea-based wind farms. Such a tool is based on a modular architecture, which will ease the integration of the tool in a multi-level design framework. Each module of the tool implements one or more engineering models to estimate all cost items along with the annual energy production of the farm, starting from a few pieces of information related to turbine types and dimensions, farm geometry and wind conditions.
Providing insight into what can be expected from Offshore Wind Farm Layout Optimisation
Authors: Thomson Michael , Zaaijer Michiel
Published: June 2022

This is not yet another study into better modelling or optimiser selection for OWFLO. Instead, this study aims to provide insight into what performance can be expected from offshore wind farm layout optimisation(OWFLO) and to know when further optimising is not justifiable anymore. The study consists of three parts. All three parts make use of a referent. (The definition of the term’referent’ as used here is given in the paper.) The first part uses the referent to find and understand the characteristics of the OWFLO problem. Wind farms with 9, 25 and 64 turbines have been optimised 100 times with the referent. The results show a small spread in the performance of the found optimised layouts, indicating that many local optima exist with similar performances in an OWFLO problem. The second part compares performances from optimised layouts with 25 turbines resulting from optimisations with alternative implementation choices, evaluated by the referent model. The difference in performance resulting from the alternative optimisers indicates that improvement of a state-of-the-art optimiser is not expected to lead to much better results. The third part explores the need to improve the analysis by adding a phenomenon currently not considered in OWFLO. The influence of neighbouring wind farms(NBWFs) on layout optimisation without including atmospheric stability is investigated. It is evident that adding NBWFs for accurate energy yield assessments is necessary. However, for layout optimisation, the benefit of including NBWFs is not apparent.
Operation and Maintenance Modelling for Multi Rotor Systems: Bottlenecks in Operations
Authors: McMorland Jade , Pirrie Paul , Collu Maurizio , McMillan David , Carroll James , Coraddu Andrea , Jamieson Peter
Published: June 2022

As the installed capacity of individual turbines increases, so do costs associated with manufacture and maintenance. One proposed solution to this problem is the Multi-Rotor System (MRS) which utilises many small rotors to yield the same energy capture as a single large turbine. The operational advantage of the MRS is the built in redundancy between rotors on the same structure. However, despite this advantage, an increase in number of components is likely to result in an increase in transfers. This work examines the balance between additional crew and vessel requirements for such a structure against the expected savings in downtime due to redundancy and small rotor power rating. Three scenarios are analysed to determine the distribution of the failures which contribute to downtime. The study aims to find the optimal vessel fleet which limits downtime without drastically increasing direct operational expenditure (OpEx). As site size increases, the impact of global failures, which shut down the whole asset, is lessened. However, there is a significant increase in the number of vessels required to reduce downtime to <10% of the total OpEx. While a large fleet can offer significant downtime savings, there are practical limitations and challenges which must be acknowledged.
Unsteady aerodynamic simulations of a multi-megawatt airborne wind energy reference system using computational fluid dynamics
Authors: Pynaert N , Wauters J , Crevecoeur G , Degroote J
Published: June 2022

Airborne wind energy (AWE) is an emerging technology for the conversion of wind energy into electricity by flying crosswind patterns with a tethered aircraft. Having a proper understanding of the unsteady interaction of the air with the highly dynamic system during operation is key to developing viable AWE systems. High fidelity simulation tools are needed to correctly predict these interactions, which will provide insights into the design and operation of advanced and efficient AWE systems. The research goal of this contribution is to predict the time-varying aerodynamic forces acting on a reference system, which will be achieved using high-fidelity modelling. This work contains a feasibility study that uses the Chimera overset technique to simulate aerodynamic behaviour over the complete flight trajectory. The motion of the AWE system is included by overlaying the moving body-fitted mesh attached to the AWE aircraft over the background mesh. The Chimera overset technique connects both meshes, interpolating the solution at the overset boundary. This technique has been proven to be a robust approach to simulate the motion of an AWE system in computational fluid dynamics (CFD) simulations.
Experimental Investigation on the Effect of Lateral Turbine Spacing on Interactions of Wakes
Authors: Maus J , Peinke J , Hölling M
Published: June 2022

In recent years, development in wind energy has put great attention on the manifold possibilities of increasing wind farm efficiencies. Motivated by multiple positive effects dualrotors promise at controlling and mitigating wakes, it is goal of this work to gain experimental insight on the characteristics of a double-rotor wake in dependency of the lateral spacing between the turbines. Additionally, the influence of the rotational direction of the model wind turbine showed to be of great relevance and will be studied under laminar inflow conditions between 4 – 7 ms−1. An array of 21 hot-wires at a sampling frequency of 6 kHz will be traversing until 8 D downstream of the double rotor plane to allow for one- and two-point statistics in the wake interaction region. It will be presented that both lateral distance and direction of rotation has great effect on the entrainment of undisturbed air into the dual wake. In terms of turbine control, decreasing the lateral distance increases the generated power by 4-9%. The knowledge on turbulence structures and their decay in the mixing area could be used to adapt, optimise and implement control strategies in wind farms for a better entrainment and thus an overall quicker wake recovery.
A Three-Dimensional Target Recognition Algorithm Utilizing Spatiotemporal Information Perception
Authors: Wang Hongmei , Huang Cheng
Published: June 2022

In order to solve the low accuracy of three-dimensional target recognition by traditional algorithms, an improved three-dimensional target recognition model based on spatiotemporal convolutional neural network is established in this paper. The high-level feature presentation ability of deep neural network and the spatiotemporal perception performance of sequential modelling methods are combined perfectly in this model. Triplet loss function is also introduced to improve the representation of three-dimensional moving targets under different attitudes, which effectively reduces the feature distance of the same targets under various attitudes, and increases the feature distance of different targets at the same time. The performance of different feature extractors and aggregation algorithms is compared in this paper. Experimental results demonstrated that the ResNet is more suitable for feature extraction and the local soft attention is more effective in feature aggregation. Compared with the traditional three-dimensional target recognition methods, the spatiotemporal perception model proposed in this paper can achieve higher recognition accuracy for three-dimensional target recognition. At the same time, the calculation speed of the model is fast, which is conducive for its application.
Unmanned Aerial Vehicle Visual Simultaneous Localization and Mapping: A Survey
Authors: Tian Y , Yue H , Yang B , Ren J
Published: June 2022

Simultaneous Localization and Mapping (SLAM) has been widely applied in robotics and other vision applications, such as navigation and path planning for unmanned aerial vehicles (UAVs). UAV navigation can be regarded as the process of robot planning to reach the target location safely and quickly. In order to complete the predetermined task, the drone must fully understand its state, including position, navigation speed, heading, starting point, and target position. With the rapid development of computer vision technology, vision-based navigation has become a powerful tool for autonomous navigation. A visual sensor can provide a wealth of online environmental information, has high sensitivity, strong anti-interference ability, and is suitable for perceiving dynamic environments. Most visual sensors are passive sensors, which prevent sensing systems from being detected. Compared with traditional sensors such as global positioning system (GPS), laser lightning, and ultrasonic sensors, visual SLAM can obtain rich visual information such as color, texture and depth. In this paper, a survey is provided on the development of relevant techniques of visual SLAM, visual odometry, image stabilization and image denoising with applications to UAVs. By analyzing the existing development, some future perspectives are briefed.
Research on Pavement Crack Detection Algorithm based on Deep Residual Unet Neural Network
Authors: Yang Yalong , Zhao Zihao , Su Liangliang , Zhou Yu , Li Hui
Published: June 2022

Pavement crack is one of the main factors threatening highway safety. Rapid and accurate identification of pavement crack is very important to protect highway. Traditional image processing methods have been difficult to meet the requirements of practical engineering. So, this paper proposes an improved pavement crack detection method based on deep convolution neural network model, to overcome the inaccurate segmentation caused by complex background and fuzzy crack edge in asphalt pavement image, which is an encoder-decoder network structure, and integrates the idea of semantic segmentation into image detection. The encoder of the proposed method adopts the ResNet’s residual structure to strengthen image feature extraction, the decoder uses transpose convolution from the original UNet structure for up sampling, and integrates the encoder features with the up sampled features after cutting, Strengthen the learning of network features. Experimental results show that the F1 score, Precision and Recall are significantly improved on the asphalt crack dataset. Compared with the traditional algorithm, the proposed algorithm can segment the cracks better and improve the accuracy of pavement crack detection.
Fire Detection and Localization Method Based on Deep Learning in Video Surveillance
Authors: Fang Q S , Peng Z , Yan P
Published: June 2022

Fire detection and localization in video surveillance had become a particularly important part of disaster rescue. Considering the fire detection and localization of slow detection speed, low detection accuracy, and low localization precision in video surveillance, we proposed a fire detection and localization method based on deep learning. The first thing we improved the SuperPoint method to extract video keyframe in video surveillance. The next thing we employed Convolutional Neural Network (CNN) model to detect the fire on the extracted video keyframes. The last thing we located the fire via superpixel and CNN on the extracted video keyframes which broke out a fire. The experimental results on open fire dataset revealed that the recall of keyframe extraction reached 0.83, the precision of fire detection reached 0.96 and the F1-score of fire localization reached 0.90. Our method realized rapid and accurate detection and precise localization of fire in video surveillance.
Prediction Method of Biological Fermentation Data Based on Deep Neural Network
Authors: Li Kang , Jiang Yizhang
Published: June 2022

This paper proposes a (Takagi-Sugeno-Kang) TSK fuzzy regression model that based on self-supervised learning and deep autoencoder to predict and monitor the real-time concentration of each ingredient in the fermentation process. The entire model consists of the following steps: obtaining and preprocessing sample spectral data to obtain a training set; using the training set to train a self-supervised feature extraction network model to optimize the parameters of the feature extraction network model; training the autoencoder network model to establish a dimensionality reduction model by using the feature-extracted data; performing TSK fuzzy regression on the data selected by the dimensionality reduction model to establish a concentration prediction model; inputting the spectral data of the solution to be tested to predict the concentration of the solution. Combined with the deep autoencoder feature extraction method of self-supervised learning, our model can not only construct a more complex nonlinear map than the traditional principal component analysis (PCA), but also ensure that the extracted features have semantic information that is beneficial to the subsequent regression prediction method. Combined with TSK regression prediction, our model can avoid the problem of excessive spectral data dimension and redundant information, and can give accurate and interpretable results.
YOLO-A2G: An air-to-ground high-precision object detection algorithm based on YOLOv5
Authors: Li Lei , Yao Ziyu , Miao Zhiyong , Qiu Xuyi , Yang Xizhong
Published: June 2022

Air-to-ground object detection is playing an increasingly important role in a variety of ground awareness and cognitive missions such as fighter aircraft attempting to assault and defend ground defense barrier fortifications and strike and destroy ground objects. However, air-to-ground object detection becomes very challenging due to the insufficient number of battlefield samples in air-to-ground imaging, many ground background disturbances and large-scale variation. In this paper, an improved air-to-ground object detection algorithm, YOLO-A2G, is proposed to solve this problem based on YOLOv5. In YOLO-A2G, firstly, in response to the insufficient number of samples, we used the direct and inverse Visual Focus (VF) affine a data augmentation algorithm to enrich and expand the samples in addition to the original data augmentation algorithm of YOLOv5. We then introduced the Coordinate Attention (CA) mechanism into the head network of YOLOv5 to autonomously learn explicit and implicit knowledge for the purpose of feature focusing and redundancy removal. Finally, in the post-processing stage after the network prediction, we used Weighted Boxes Fusion (WBF) instead of the traditional NMS to achieve spatial scale fusion. We performed an experimental validation using the Air-to-Ground (A2G) dataset and mAP of YOLO-A2G reached 94%.
Sketch Based Image Retrieval for Architecture Images with Siamese Swin Transformer
Authors: Xu Yuxin , Yan Yuyao , Lin Yiming , Yang Xi , Huang Kaizhu
Published: June 2022

Sketch-based image retrieval (SBIR) is an image retrieval task that takes a sketch as input and outputs colour images matching the sketch. Most recent SBIR methods utilise deep learning methods with complicated network designs, which are resource-intensive for practical use. This paper proposes a novel compact framework that takes the siamese network with image view angle information, targeting the SBIR task for architecture images. In particular, the proposed siamese network engages a compact SwinTiny transformer as the backbone encoder. View angle information of the architecture image is fed to the model to further improve search accuracy. To cope with the insufficient sketches issue, simulated building sketches are used in training, which are generated by a pre-trained edge extractor. Experiments show that our model achieves 0.859 top-one accuracy exceeding many baseline models for an architecture retrieval task.
Quantum tomography and Schwinger’s picture of quantum mechanics*
Authors: Ciaglia F M , Di Cosmo F , Ibort A , Marmo G
Published: June 2022

In this paper the problem of tomographic reconstruction of states is investigated within the so-called Schwinger’s picture of quantum mechanics in which a groupoid is associated with every quantum system. The attention is focussed on spin tomography: in this context the groupoid of interest is the groupoid of pairs over a finite set. In a nutshell, this groupoid is made up of transitions between all possible pairs of outcomes belonging to a finite set. In addition, these transitions possess a partial composition rule, generalizing the notion of groups. The main goal of the paper consists in providing a reconstruction formula for states on the groupoid-algebra associated with the observables of the system. Using the group of bisections of this groupoid, which are special subsets in one-to-one correspondence with the outcomes, a frame is defined and it is used to prove the validity of the tomographic reconstruction. The special case of the set of outcomes being the set of integers modulo n, with n odd prime, is considered in detail. In this case the subgroup of discrete affine linear transformations, whose graphs are linear subspaces of the groupoid, provides a quorum in close analogy with the continuous case.
Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit
Authors: Tegtmeier Riley C , Ferris William S , Bayouth John E , Culberson Wesley S
Published: June 2022

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system. Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey’s honestly significant difference post-hoc tests were utilized for statistical analysis. Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results. Significance. An overall improvement in image quality for metrics most important to IGRT and ART—mainly image noise and contrast—was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.
Electron beam lithography on non-planar, suspended, 3D AFM cantilever for nanoscale thermal probing
Authors: Swami R , Julié G , Singhal D , Paterson J , Maire J , Le-Denmat S , Motte J F , Gomès S , Bourgeois O
Published: June 2022

Electron beam lithography (EBL) on non-planar, suspended, curved or bent surfaces is still one of the most frequently stated problems for fabricating novel and innovative nano-devices and sensors for future technologies. Although spin coating is the most widespread technique for electron resist (e-resist) deposition on 2D or flat surfaces, it is inadequate for suspended and 3D architectures because of its lack of uniformity. In this work, we use a thermally evaporated electron sensitive resist the QSR-5 and study its sensitivity and contrast behaviour using EBL. We show the feasibility of utilizing the resist for patterning objects on non-planar, suspended structures via EBL and dry etching processes. We demonstrate the integration of metal or any kind of thin films at the apex of an atomic force microscopy (AFM) tip. This is showing the great potential of this technology in various fields, such as magnetism, electronic, photonics, phononics and other fields related to near field microscopy using AFM probe like for instance scanning thermal microscopy.
CLIPreg: constructing translational regulatory networks from CLIP-, Ribo- and RNA-seq
Authors: Kerouanton Baptiste , Schäfer Sebastian , Ho Lena , Chothani Sonia , Rackham Owen J L
Published: June 2022

Motivation

The creation and analysis of gene regulatory networks have been the focus of bioinformatics research and underpins much of what is known about gene regulation. However, as a result of a bias in the availability of data types that are collected, the vast majority of gene regulatory network resources and tools have focused on either transcriptional regulation or protein–protein interactions. This has left other areas of regulation, for instance, translational regulation, vastly underrepresented despite them having been shown to play a critical role in both health and disease.

Results

In order to address this, we have developed CLIPreg, a package that integrates RNA, Ribo and CLIP- sequencing data in order to construct translational regulatory networks coordinated by RNA-binding proteins and micro-RNAs. This is the first tool of its type to be created, allowing for detailed investigation into a previously unseen layer of regulation.

Availability and implementation

CLIPreg is available at https://github.com/SGDDNB/CLIPreg.

Supplementary information

Supplementary data are available at Bioinformatics online.
scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data
Authors: Shen Siqi , Zheng Ye , Keleş Sündüz
Published: June 2022

Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.

Availability and implementation

scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html.

Supplementary information

Supplementary data are available at Bioinformatics online.
The European Union Statistics on Income and Living Conditions after 15 Years
Authors: Wirth Heike , Pforr Klaus
Published: June 2022

This data brief describes the European Union Statistics on Income and Living Conditions (EU-SILC). Detailed data on income and taxes are collected, as well as information on material deprivation, labour, housing, childcare, health, access to and use of services, and education. Although primarily a social policy instrument that addresses the information needs of policymakers and is used for social monitoring at the European level, EU-SILC is also closely geared to the needs of researchers and provides an excellent database for evidence-based research on a wide variety of aspects of income, income poverty, material poverty, health, and well-being in Europe. EU-SILC is composed of national probability sample surveys and is conducted annually. The target population comprises private households. Observation units are households and all current household members. EU-SILC provides cross-sectional and longitudinal data. The data are composed of a fixed core module, and annually changing ad-hoc modules. Launched in 2003 and revised with effect from 2021, EU-SILC is currently implemented in all EU Member States and in 11 non-EU countries. During the revision process, many suggestions from the research community were incorporated.
Bacterial–viral interactions: a factor that facilitates transmission heterogeneities
Authors: Stein Richard A , Bianchini Emilia Claire
Published: June 2022

The transmission of infectious diseases is characterized by heterogeneities that are shaped by the host, the pathogen, and the environment. Extreme forms of these heterogeneities are called super-spreading events. Transmission heterogeneities are usually identified retrospectively, but their contribution to the dynamics of outbreaks makes the ability to predict them valuable for science, medicine, and public health. Previous studies identified several factors that facilitate super-spreading; one of them is the interaction between bacteria and viruses within a host. The heightened dispersal of bacteria colonizing the nasal cavity during an upper respiratory viral infection, and the increased shedding of HIV-1 from the urogenital tract during a sexually transmitted bacterial infection, are among the most extensively studied examples of transmission heterogeneities that result from bacterial–viral interactions. Interrogating these transmission heterogeneities, and elucidating the underlying cellular and molecular mechanisms, are part of much-needed efforts to guide public health interventions, in areas that range from predicting or controlling the population transmission of respiratory pathogens, to limiting the spread of sexually transmitted infections, and tailoring vaccination initiatives with live attenuated vaccines.
Comparative analysis of the testes from wild-type and Alkbh5-knockout mice using single-cell RNA sequencing
Authors: Hong Shihao , Shen Xiaozhong , Luo Chunhai , Sun Fei
Published: June 2022

The RNA demethylase ALKBH5 is regarded as the “eraser” in N6-methyladenosine modification. ALKBH5 deficiency causes male infertility in mice; however, the mechanisms that confer disruption of spermatogenesis are not completely clear. In this study, we profiled testis samples from wild-type and Alkbh5-knockout mice using single-cell RNA sequencing. We obtained single-cell RNA sequencing data of 5,596 and 6,816 testis cells from a wild-type and a knockout mouse, respectively. There were differences detected between the transcriptional profiles of the groups at various germ cell developmental stages. This ranged from the development of spermatogonia to sperm cells, in macrophages, Sertoli cells, and Leydig cells. We identified the differentially expressed genes related to spermatogenesis in germ cells and somatic cells (Sertoli cells and Leydig cells) and evaluated their functions and associated pathways, such as chromatin-related functional pathways, through gene ontology enrichment analysis. This study provides the first single-cell RNA sequencing profile of the testes of ALKBH5-deficient mice. This highlights that ALKBH5 is an important gene for germ cell development and spermatogenesis and offers new molecular mechanistic insights. These findings could provide the basis for further research into the causes and treatment of male infertility.
Spodoptera littoralis genome mining brings insights on the dynamic of expansion of gustatory receptors in polyphagous noctuidae
Authors: Meslin Camille , Mainet Pauline , Montagné Nicolas , Robin Stéphanie , Legeai Fabrice , Bretaudeau Anthony , Johnston J Spencer , Koutroumpa Fotini , Persyn Emma , Monsempès Christelle , François Marie-Christine , Jacquin-Joly Emmanuelle
Published: June 2022

The bitter taste, triggered via gustatory receptors, serves as an important natural defense against the ingestion of poisonous foods in animals, and the increased host breadth is usually linked to an increase in the number of gustatory receptor genes. This has been especially observed in polyphagous insect species, such as noctuid species from the Spodoptera genus. However, the dynamic and physical mechanisms leading to these gene expansions and the evolutionary pressures behind them remain elusive. Among major drivers of genome dynamics are the transposable elements but, surprisingly, their potential role in insect gustatory receptor expansion has not been considered yet. In this work, we hypothesized that transposable elements and possibly positive selection would be involved in the highly dynamic evolution of gustatory receptor in Spodoptera spp. We first sequenced de novo the full 465 Mb genome of S. littoralis, and manually annotated the main chemosensory genes, including a large repertoire of 373 gustatory receptor genes (including 19 pseudogenes). We also improved the completeness of S. frugiperda and S. litura gustatory receptor gene repertoires. Then, we annotated transposable elements and revealed that a particular category of class I retrotransposons, the SINE transposons, was significantly enriched in the vicinity of gustatory receptor gene clusters, suggesting a transposon-mediated mechanism for the formation of these clusters. Selection pressure analyses indicated that positive selection within the gustatory receptor gene family is cryptic, only 7 receptors being identified as positively selected. Altogether, our data provide a new good quality Spodoptera genome, pinpoint interesting gustatory receptor candidates for further functional studies and bring valuable genomic information on the mechanisms of gustatory receptor expansions in polyphagous insect species.
A look-ahead approach to maximizing present value of genetic gains in genomic selection
Authors: Zhang Zerui , Wang Lizhi
Published: June 2022

Look-ahead selection is a sophisticated yet effective algorithm for genomic selection, which optimizes not only the selection of breeding parents but also mating strategy and resource allocation by anticipating the implications of crosses in a prespecified future target generation. Simulation results using maize datasets have suggested that look-ahead selection is able to significantly accelerate genetic gain in the target generation while maintaining genetic diversity. In this paper, we propose a new algorithm to address the limitations of look-ahead selection, including the difficulty in specifying a meaningful deadline in a continuous breeding process and slow growth of genetic gain in early generations. This new algorithm uses the present value of genetic gains as the breeding objective, converting genetic gains realized in different generations to the current generation using a discount rate, similar to using the interest rate to measure the time value of cash flows incurred at different time points. By using the look-ahead techniques to anticipate the future gametes and thus present value of future genetic gains, this algorithm yields a better trade-off between short-term and long-term benefits. Results from simulation experiments showed that the new algorithm can achieve higher genetic gains in early generations and a continuously growing trajectory as opposed to the look-ahead selection algorithm, which features a slow progress in early generations and a growth spike right before the deadline.
Invariant Galton–Watson branching process for earthquake occurrence
Authors: Kovchegov Yevgeniy , Zaliapin Ilya , Ben-Zion Yehuda
Published: June 2022

We propose a theoretical modelling framework for earthquake occurrence and clustering based on a family of invariant Galton–Watson (IGW) stochastic branching processes. The IGW process is a rigorously defined approximation to imprecisely observed or incorrectly estimated earthquake clusters modelled by Galton–Watson branching processes, including the Epidemic Type Aftershock Sequence (ETAS) model. The theory of IGW processes yields explicit distributions for multiple cluster attributes, including magnitude-dependent and magnitude-independent offspring number, cluster size and cluster combinatorial depth. Analysis of the observed seismicity in southern California demonstrates that the IGW model provides a close fit to the observed earthquake clusters. The estimated IGW parameters and derived statistics are robust with respect to the catalogue lower cut-off magnitude. The proposed model facilitates analyses of multiple quantities of seismicity based on self-similar tree attributes, and may be used to assess the proximity of seismicity to criticality.
Interannual variability in size-selective winter mortality of young-of-the-year striped bass
Authors: Peres Henrique A , Robert Dominique , Mainguy Julien , Sirois Pascal
Published: June 2022

Early life stages of fish are characterized by high size-selective mortality rates, with selection generally acting against smaller, slow-growing individuals. Here, we investigate, for the St. Lawrence River striped bass (Morone saxatilis) population, how size of young-of-the-year juveniles (YOYs) affected survival from the pre-wintering period until the following spring, by comparing their otolith daily growth trajectory to that of one-year-old juveniles (OYOs). Otolith growth in the first 50 d after hatch was faster in post- than in pre-winter juveniles in both years, indicating that fast-growing individuals were more likely to survive to the next spring. A larger back-calculated size at age 1 in the 2016 year class compared to that observed in 2017 also suggests interannual variability in size-selective overwinter survival. Our results indicate that the design of YOY abundance surveys aimed at predicting annual recruitment strength needs to account for the effect of size-dependent mortality until the end of the first winter of life, as high abundance of relatively small YOYs in autumn may not necessarily translate into a large number of OYOs in the following spring and thus into high recruitment.
A secondary data analysis of a cluster randomized controlled trial: improved cookstoves associated with reduction in incidence of low birthweight in rural Malawi
Authors: Best Rebecca , Malava Jullita , Dube Albert , Katundu Cynthia , Kalobekamo Fredrick , Mortimer Kevin , Gordon Stephen B , Nyirenda Moffat , Crampin Amelia , McLean Estelle
Published: June 2022

Background

In northern rural Malawi, the majority of households cook using open fires and there is also a high burden of adverse birth outcomes. The use of open fires or highly polluting cookstoves is associated with low birthweight in babies. There is mixed evidence on whether implementation of cleaner burning cookstoves reduces the number of babies born with low birthweight.

Methods

This is a secondary analysis of a cluster randomized control trial in Malawi, conducted over 2014–17. Households were randomized to receive improved cookstoves or to continue current practices. For this analysis, the primary outcome was low birthweight in households under routine demographic surveillance, among births occurring within the trial time frame (N = 4010). A subset of data with stricter exposure definitions respecting the original randomized allocation was also analysed (N = 1050). A causal, forwards modelling approach was used.

Results

The main dataset showed evidence of effect of the intervention on low birthweight [adjusted odds ratio (aOR) 0.69; 95% CI 0.48–0.99, n = 2788). The subset analysis lacked power to provide evidence of association between improved cookstoves and low birthweight in the stricter exposure definition (aOR 0.62; 95% CI 0.35–1.09, n = 932).

Conclusions

This study provides some evidence that an improved cookstove intervention in rural Malawi reduced the number of babies born with low birthweight by 30%. This direction of the effect was also seen in the subset analysis. The analysis suggests that the intervention reduced the number of infants born prematurely or with intra-uterine growth restriction, indicating that improved cookstoves could be a useful maternal health intervention.
Strategic psychology and the study of China’s whole-of-nation strategy
Authors: Chung Youngjune
Published: June 2022

This study explores conceptual variants underpinning the prolific threat of China’s global information operations, shedding light on the under-examined role of strategic psychology in China’s quest for global primacy. Ancient stratagems underlying the Chinese cultural identity and character are applied through a framework to provide clarifying insights into the motivations, intentions, and capacities of China’s pre- and nonkinetic actions. The aim is to identify the individual rationales and cognitive mechanisms that underlie the vast array of China’s national instruments short of war and provide an indigenous, localized perspective to fully understand China’s whole-of-nation strategy.
Automated patient self-scheduling: case study
Authors: Woodcock Elizabeth , Sen Aditi , Weiner Jonathan
Published: June 2022

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments were lower and cancelations were higher for self-scheduled patients. An examination of characteristics, benefits, and usage of automated self-scheduling provides insight to those organizations contemplating the implementation or expansion of similar consumer-facing digital self-scheduling platforms.
Conceptualizing clinical decision support as complex interventions: a meta-analysis of comparative effectiveness trials
Authors: Reese Thomas J , Liu Siru , Steitz Bryan , McCoy Allison , Russo Elise , Koh Brian , Ancker Jessica , Wright Adam
Published: June 2022

Objectives

Complex interventions with multiple components and behavior change strategies are increasingly implemented as a form of clinical decision support (CDS) using native electronic health record functionality. Objectives of this study were, therefore, to (1) identify the proportion of randomized controlled trials with CDS interventions that were complex, (2) describe common gaps in the reporting of complexity in CDS research, and (3) determine the impact of increased complexity on CDS effectiveness.

Materials and Methods

To assess CDS complexity and identify reporting gaps for characterizing CDS interventions, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting tool for complex interventions. We evaluated the effect of increased complexity using random-effects meta-analysis.

Results

Most included studies evaluated a complex CDS intervention (76%). No studies described use of analytical frameworks or causal pathways. Two studies discussed use of theory but only one fully described the rationale and put it in context of a behavior change. A small but positive effect (standardized mean difference, 0.147; 95% CI, 0.039–0.255; P < .01) in favor of increasing intervention complexity was observed.

Discussion

While most CDS studies should classify interventions as complex, opportunities persist for documenting and providing resources in a manner that would enable CDS interventions to be replicated and adapted. Unless reporting of the design, implementation, and evaluation of CDS interventions improves, only slight benefits can be expected.

Conclusion

Conceptualizing CDS as complex interventions may help convey the careful attention that is needed to ensure these interventions are contextually and theoretically informed.
Effective length of bamboo-like stiffened hollow cylindrical structures
Authors: Nishiyama Ryo , Sato Motohiro
Published: June 2022

Hollow cylindrical structures are susceptible to local buckling because they flatten and significantly reduce their stiffness when they bend. Therefore, many previous studies aimed to improve the strength of pipelines and building structures were conducted. Our research group has focused on bamboo and has theoretically proven that stiffness anisotropy caused by bamboo's unique nodes and vascular bundles enhances the stiffness of cylindrical structures. In this study, to investigate this analytically, we carried out a finite-element analysis and succeeded in deriving a new dimensionless parameter that the stiffening effect of an anisotropic consideration. This result is applicable to a wide range of cylindrical structures, from thin-walled to thick-walled, and it is expected that bamboo-inspired bionic designs will be proposed in the future.
An alternative splicing isoform of wheat TaYRG1 resistance protein activates immunity by interacting with dynamin-related proteins
Authors: Zhang Lu , Liu Yuanming , Wang Qiaohui , Wang Chao , Lv Shikai , Wang Yanzhen , Wang Jianfeng , Wang Yajuan , Yuan Jing , Zhang Hong , Kang Zhensheng , Ji Wanquan
Published: June 2022

Wheat (Triticum aestivum) is a commercially important crop and its production is seriously threatened by the fungal pathogen Puccinia striiformis f. sp. tritici West (Pst). Resistance (R) genes are critical factors that facilitate plant immune responses. Here, we report a wheat R gene NB-ARC-LRR ortholog, TaYRG1, that is associated with distinct alternative splicing events in wheat infected by Pst. The native splice variant, TaYRG1.6, encodes internal-motif-deleted polypeptides with the same N- and C-termini as TaYRG1.1, resulting in gain of function. Transient expression of protein variants in Nicotiana benthamiana showed that the NB and ARC domains, and TaYRG1.6 (half LRR domain), stimulate robust elicitor-independent cell death based on a signal peptide, although the activity was negatively modulated by the CC and complete LRR domains. Furthermore, molecular genetic analyses indicated that TaYRG1.6 enhanced resistance to Pst in wheat. Moreover, we provide multiple lines of evidence that TaYRG1.6 interacts with a dynamin-related protein, TaDrp1. Proteome profiling suggested that the TaYRG1.6-TaDrp1-DNM complex in the membrane trafficking systems may trigger cell death by mobilizing lipid and kinase signaling in the endocytosis pathway. Our findings reveal a unique mechanism by which TaYRG1 activates cell death and enhances disease resistance by reconfiguring protein structure through alternative splicing.
Reading Arab American Literary Variations: An Interview with Rajia Hassib
Authors: Berrebbah Ishak
Published: June 2022

In this interview, Rajia Hassib provides answers to various questions related to her fiction, particularly In the Language of Miracles (2015) and A Pure Heart (2020). This interview, and the discussion that stems from it, introduce critical insights into contemporary Arab American women’s literature and thoughts about the multiple aspects that shape and characterize Arab identity in the US diaspora. The interview contributes to existing scholarship concerning the Arab American diaspora and the study of its literature.