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Abstract Continuous‐flow intersections (CFI), also known as displaced left‐turn (DLT) intersections, aim to improve the efficiency and safety of traffic junctions. A CFI introduces additional cross‐over intersections upstream of the main intersection to split the left‐turn flow from the through movement before it arrives at the main intersection which decreases the number of conflict points between left‐turn and through movements. This study develops and examine a two‐step optimization model for CFI traffic signal control design and demonstrates its performance across more than 300 different travel demand scenarios. The proposed model is compared against a state‐of‐practice CFI signal control model as a benchmark. Microsimulation results suggest that the proposed model reduces average delay by 17% and average queue length by 32% for a full CFI compared with the benchmark signal control model.

Abstract Background The incidence of chronic diseases is on the rise worldwide, with a high mortality rate in China, posing a serious threat to the health-related quality of life (HRQoL) of middle-aged and older adults. This study explores the association between chronic diseases and the HRQoL of middle-aged and older adults, as well as the role of physical activity (PA) and degree of digitization in this relationship. Methods The data used in this study was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which included 13,620 middle-aged and older Chinese adults (≥ 45 years). The study utilized correlation analysis, and bootstrapping to investigate the mediating role of PA and the moderating influence of the degree of digitization. Data analysis was conducted using SPSS 26.0. Results The study findings indicate that the severity of chronic disease has a significant negative predictive effect on HRQoL (PCS, physical component summary; MCS, mental component summary) (PCS: β = -2.515, p

Abstract A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

IntroductionThe purpose of this study was to explore the associations between ultra-processed foods (UPF), unprocessed or minimally processed foods (UMFs) with the quality of life (QoL) in Chinese adolescents.MethodsThe study included a baseline survey in 2021 (T1) and a follow-up survey in 2022 (T2), with a total of 3,206 participants, including 1,510 males (Age ± SE: 13.62 ± 1.69) and 1,696 females (Age ± SE: 14.09 ± 1.85). QoL was measured using the Quality of Life Scale for Children and Adolescents. All adolescents were asked to recall the foods they had eaten in the past week, which was classified using the NOVA system.ResultsInstant noodles, sugary drinks, and fried foods had a negative impact on adolescents’ QoL. Snacks or desserts only had a negative impact on the girls’ QoL. However, processed meats had a positive impact on the QoL of all adolescents. Fast food was beneficial for girls’ QoL. Fruits had a positive impact on the QoL of all adolescents. Green leafy vegetables, red and orange vegetables, fish, shrimp, or other seafood had a positive impact on the QoL of girls. Fresh poultry, pork, beef, and other meats had a positive impact on boys’ QoL. Soy products were detrimental to girls’ QoL. Tubers had a negative impact on boys’ QoL. Potatoes had a negative effect on the all adolescents’ QoL.DiscussionThis study further expands the understanding of the relationship between UPF, UMFs, and QoL. It provides new insights and evidence for improving the QoL of adolescents.

BackgroundThis study aims to explore the effects of physical activity on adolescents' social skills and antisocial behavior, as well as gender and only child status differences among Chinese adolescents.MethodsWe used longitudinal data, collecting baseline data for 2021–2022 and follow-up in 2022–2023. A total of 3,206 students were enrolled, including 1,510 males (Age ± SE: 13.62 ± 1.69) and 1,696 females (Age ± SE: 14.09 ± 1.85), 1,339 only children (Age ± SE: 13.76 ± 1.95), and 1,867 non-only children (Age ± SE: 13.95 ± 1.66). The fixed effects model was used to identify the effects of different types of physical activity on adolescent social behavior and the heterogeneity analysis.Results(1) We found that skipping rope (β = 2.284, t = 5.76), walking (β = 3.495, t = 9.53), cycling (β = 1.271, t = 3.21), jogging (β = 2.614, t = 5.92), and badminton (β = 1.409, t = 2.96) had a positive impact on adolescents' social skills. Tag games (β = −1.615, t = −3.83), swimming (β = −2.862, t = −4.42), dancing (β = −1.11, t = −2.29), and skiing (β = −2.771, t = −3.27) had a negative impact on adolescents' social skills. Skipping rope (β = −1.596, t = −5.86), walking (β = −1.814, t = −7.44), cycling (β = −1.066, t = −4.07), and jogging (β = −1.617, t = −5.30) tended to reduce adolescents' antisocial behavior. Tag games (β = 1.685, t = 5.54), swimming (β = 0.947, t = 2.06), ice skating (β = 1.772, t = 2.71), and skiing (β = 1.468, t = 2.31) tended to increase adolescents' antisocial behavior. In addition, we found that these effects differ by gender and only child status. (2) Boys had significantly higher participation rates in activities. Only children had higher participation rates in skipping rope, baseball or softball, and volleyball, but non-only children had higher participation rates in walking, cycling, jogging, other aerobic exercises, and badminton. (3) Girls had higher social skills, but boys and only children had significantly higher rates of antisocial behavior.ConclusionOur results provide a novel understanding that can inform interventions and improvements in social behavior among Chinese adolescents. Stakeholders such as educators, policymakers, and health professionals can develop more tailored and effective strategies to promote positive social development among adolescents.

Abstract Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Current methods to reanalyze bulk RNA-seq at spatially resolved single-cell resolution have limitations. Here, the authors develop Bulk2Space, a spatial deconvolution algorithm using single-cell and spatial transcriptomics as references, providing new insights into spatial heterogeneity within bulk tissue.

Summary: Many tools have been developed to measure the degree of similarity between gene duplicates within and between species. Here, we present HSDecipher, a bioinformatics pipeline to assist users in the analysis and visualization of highly similar duplicate genes (HSDs). We describe the steps for analysis of HSDs statistics, expanding HSD gene sets, and visualizing the results of comparative genomic analyses. HSDecipher represents a useful tool for researchers exploring the evolution of duplicate genes in select eukaryotic species.For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021)1 and Zhang et al. (2022).2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Spin density waves are a spatial modulation of the spin, and can be either commensurate or incommensurate with the crystal lattice. Here, Hu et al. use spin-polarised scanning tunnelling microscopy to observe the incommensurate spin density wave on cleaned Chromium surface.

X-ray imaging is proven effective in the visualization of defects inside the Aluminum Conductor Composite Core (ACCC) wires. Although object detection pipelines have been extensively considered in the nondestructive testing tasks, the difficulty in obtaining defect samples has become the main obstacle to the application of such methods in the task of automatic defect detection for ACCC wires X-ray images. In this paper, we conducted a new semi-supervised approach based on anomaly detection. Different from the commonly used supervised methods, the proposed method requires only samples without defects for the learning process, therefore we are no longer limited by the insufficient and unbalanced defect samples. Experimental results show that the accuracy of the proposed method is up to 0.761, which proves the effectiveness of the method in the automatic defect detection of ACCC wires X-ray images.

The industrial and computing research context revolutionized in various directions during the last decades. The blockchain-based smart contract embraced as a significant research interest due to its distinguishing features such as decentralized storage of transactions, autonomous execution of contract codes, and decentralized establishment of the trust. Blockchain-based smart contracts can transform the working architecture of almost all industries towards elevated service standards. The use cases of blockchain based smart contracts range from industrial applications such as cryptocurrency systems towards logistics, agriculture, real estate, energy trading and so forth. The decentralization concept of blockchain is one of the biggest leaps in technology research since future computing got a super momentum towards the Internet of Things (IoT) and edge computing. A plethora of research is in progress to investigate the opportunities for the applicability of smart contracts and blockchain technologies to various industries. It is important to identify the technical aspects of blockchain-based smart contracts to further improve and sharpen the capabilities which they already owed. This survey is conducted to identify the significant technical aspects of blockchain-based smart contracts with the associated future research directions.

Summary: Various bioinformatics protocols have been developed for trimming the number of operational taxonomic units (OTUs) in phylogenetic datasets, but they typically require significant manual intervention. Here we present TreeTuner, a semiautomated pipeline that allows both coarse and fine-scale tuning of large protein sequence phylogenetic datasets via the minimization of OTU redundancy. TreeTuner facilitates preliminary investigation of such datasets as well as more rigorous downstream analysis of specific subsets of OTUs.For complete details on the use and execution of this protocol, please refer to Maruyama et al. (2013) and Sibbald et al. (2019).

Gene duplication is an important evolutionary mechanism capable of providing new genetic material for adaptive and nonadaptive evolution. However, bioinformatics tools for identifying duplicate genes are often limited to the detection of paralogs in multiple species or to specific types of gene duplicates, such as retrocopies. Here, we present a user-friendly, BLAST-based web tool, called HSDFinder, which can identify, annotate, categorize, and visualize highly similar duplicate genes (HSDs) in eukaryotic nuclear genomes. HSDFinder includes an online heatmap plotting option, allowing users to compare HSDs among different species and visualize the results in different Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional categories. The external software requirements are BLAST, InterProScan, and KEGG. The utility of HSDFinder was tested on various model eukaryotic species, including Chlamydomonas reinhardtii, Arabidopsis thaliana, Oryza sativa, and Zea mays as well as the psychrophilic green alga Chlamydomonas sp. UWO241, and was proven to be a practical and accurate tool for gene duplication analyses. The web tool is free to use at http://hsdfinder.com. Documentation and tutorials can be found via the GitHub: https://github.com/zx0223winner/HSDFinder.

Summary: Annotating protein-coding genes can be challenging, especially when searching for the best hits against multiple functional databases. This is partly because of ''bad words'' appearing as top hits, such as hypothetical or uncharacterized proteins. To help alleviate some of these issues, we designed a bioinformatics tool called NoBadWordsCombiner, which efficiently merges the hits from various databases, strengthening gene definitions by minimizing functional descriptions containing ''bad words.'' Unlike other available tools, NoBadWordsCombiner is user friendly, but it does require users to have some general bioinformatics skills, including a basic understanding of the BLAST package and dash shell in Linux/Unix environments.For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021a).

Summary: Although gene duplications have been documented in many species, the precise numbers of highly similar duplicated genes (HSDs) in eukaryotic nuclear genomes remain largely unknown and can be time-consuming to explore. We developed HSDFinder to identify, categorize, and visualize HSDs in eukaryotic nuclear genomes using protein family domains and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In contrast to existing tools, HSDFinder allows users to compare HSDs among different species and visualize results in different KEGG pathway functional categories via heatmap plotting.For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021).

Digital banking as an essential service can be hard to access in remote, rural regions where the network connectivity is unavailable or intermittent. The payment operators like Visa and Mastercard often face difficulties reaching these remote, rural areas. Although micro-banking has been made possible by short message service or unstructured supplementary service data messages in some places, their security flaws and session-based nature prevent them from wider adoption. Global-level cryptocurrencies enable low-cost, secure, and pervasive money transferring among distributed peers, but are still limited in their ability to reach people in remote communities. We propose a blockchain-based digital payment scheme that can deliver reliable services on top of unreliable networks in remote regions. We focus on a scenario where a community-run base station provides reliable local network connectivity while intermittently connects to the broader Internet. We take advantage of the distributed verification guarantees of the Blockchain technology for financial transaction verification and leverage smart contracts for secure service management. In the proposed system, payment operators deploy multiple proxy nodes that are intermittently connected to the remote communities where the local blockchain networks, such as Ethereum are composed of miners, vendors, and regular users. Through probabilistic modeling, we devise design parameters for the blockchain network to realize robust operation over the top of the unreliable network. Furthermore, we show that the transaction processing time will not be significantly impacted due to the network unreliability through extensive emulations on a private Ethereum network. Finally, we demonstrate the practical feasibility of the proposed system by developing Near Field Communication (NFC)-enabled payment gateways on Raspberry-Pis, a mobile wallet application and mining nodes on off-the-shelf computers.

Abstract Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

Abstract X-ray computed tomography (CT) has been widely used to provide patient-specific anatomical information in the forms of tissue attenuation. However, the cumulative radiation induced in CT scan has raised extensive concerns in recently years. How to maintain reconstruction image quality is a major challenge for low-dose CT (LDCT) imaging. Generally, LDCT imaging can be greatly improved by incorporating prior knowledge in some specific forms. A joint estimation framework termed discriminative prior-prior image constrained compressed sensing (DP-PICCS) reconstruction is proposed in this paper. This DP-PICCS algorithm utilizes discriminative prior knowledge via two feature dictionary constraints which built on atoms from the samples of tissue attenuation feature patches and noise-artifacts residual feature patches, respectively. Also, the prior image construction relies on a discriminative feature representation (DFR) processing by two feature dictionary. Its comparison to other competing methods through experiments on low-dose projections acquired from torso phantom simulation study and clinical abdomen study demonstrated that the DP-PICCS method achieved promising improvement in terms of the effectively-suppressed noise and the well-retained structures.

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.