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Improper pain management leads to severe physical or mental consequences, including suffering, a negative impact on quality of life, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is a challenging task because different individuals experience pain differently. To overcome this, many researchers in the field have employed machine learning models to evaluate pain intensity objectively using physiological signals. However, these efforts have primarily focused on pain point estimation, disregarding inherent uncertainty and variability in the data and model. A point estimate, which provides only partial information, is not sufficient for sound clinical decision-making. This study proposes a neural network-based method for objective pain interval estimation, and quantification of uncertainty. Our approach, which enables objective pain intensity estimation with desired confidence probabilities, affords clinicians a better understanding of a person’s pain intensity. We explored three distinct algorithms: the bootstrap method, lower and upper bound estimation ( Loss L ) optimized by genetic algorithm, and modified lower and upper bound estimation ( Loss S ) optimized by gradient descent algorithm. Our empirical results demonstrate that Loss S outperforms the other two by providing narrower prediction intervals. For 50%, 75%, 85%, and 95% prediction interval coverage probability, Loss S provides average interval widths that are 22.4%, 7.9%, 16.7%, and 9.1% narrower than those of Loss L , and 19.3%, 21.1%, 23.6%, and 26.9% narrower than those of bootstrap. As Loss S outperforms, we assessed its performance in three different model-building approaches: (1) a generalized approach using a single model for the entire population, (2) a personalized approach with separate models for each individual, and (3) a hybrid approach with models for clusters of individuals. Results demonstrate that the hybrid model-building approach provides the best performance.

Zea mays is an important genetic model for elucidating transcriptional networks. Uncertainties about the complete structure of mRNA transcripts limit the progress of research in this system. Here, using single-molecule sequencing technology, we produce 111,151 transcripts from 6 tissues capturing ∼70% of the genes annotated in maize RefGen_v3 genome. A large proportion of transcripts (57%) represent novel, sometimes tissue-specific, isoforms of known genes and 3% correspond to novel gene loci. In other cases, the identified transcripts have improved existing gene models. Averaging across all six tissues, 90% of the splice junctions are supported by short reads from matched tissues. In addition, we identified a large number of novel long non-coding RNAs and fusion transcripts and found that DNA methylation plays an important role in generating various isoforms. Our results show that characterization of the maize B73 transcriptome is far from complete, and that maize gene expression is more complex than previously thought. Zea mays is an important crop species and genetic model but uncertainties remain regarding the structure of the transcriptome. Here Wang et al . use single-molecule sequencing and size-fractionated libraries to identify novel transcripts and isoforms illustrating the complexity of maize mRNA.

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

Background The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this ‘names problem’ has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. Results The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. Conclusions We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/ .

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice ( Oryza sativa ), plus the genomes of two wild relatives ( O. rufipogon and O. punctata ), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance. Pan-genomes provide useful resources for evolutionary studies, functional genomics and breeding of cultivated plants. Here, the authors report a new rice pan-genome including 73 Asian rice and two wild relatives ( Oryza rufipogon and O. punctata ), and reveal the prevalence and scale of large inversions across the pan-genome.

Accurately assessing pain severity is essential for effective pain treatment and desirable patient outcomes. In clinical settings, pain intensity assessment relies on self-reporting methods, which are subjective to individuals and impractical for noncommunicative or critically ill patients. Previous studies have attempted to measure pain objectively using physiological responses to an external pain stimulus, assuming that the participant is free of internal body pain. However, this approach does not reflect the situation in a clinical setting, where a patient subjected to an external pain stimulus may already be experiencing internal body pain. This study investigates the hypothesis that an individual's physiological response to external pain varies in the presence of preexisting pain. We recruited 39 healthy participants aged 22-37 years, including 23 female and 16 male participants. Physiological signals, electrodermal activity, and electromyography were recorded while participants were subject to a combination of preexisting heat pain and cold pain stimuli. Feature engineering methods were applied to extract time-series features, and statistical analysis using ANOVA was conducted to assess significance. We found that the preexisting pain influences the body's physiological responses to an external pain stimulus. Several features-particularly those related to temporal statistics, successive differences, and distributions-showed statistically significant variation across varying preexisting pain conditions, with P values <.05 depending on the feature and stimulus. Our findings suggest that preexisting pain alters the body's physiological response to new pain stimuli, highlighting the importance of considering pain history in objective pain assessment models.

Background The unilateral biportal endoscopic (UBE) technique has garnered significant attention for its little paraspinal iatrogenic damage, expedited recovery, and low complication rates. This method is also applicable to open transforaminal lumbar interbody fusion (TLIF). Therefore, this study aimed to conduct a comparative analysis of the outcomes associated with unilateral biportal endoscopic transforaminal lumbar interbody fusion (UBE-TLIF), minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF), and TLIF for Meyerding grade I lumbar spondylolisthesis. Methods The study examined the outcomes of 79 patients with Meyerding grade I lumbar spondylolisthesis who underwent single-level intervertebral fusion. Clinical assessments included the measurement of pain levels using the Visual Analogue Scale (VAS) for low back and leg pain, the Oswestry Disability Index (ODI), surgical data, and demographic information. Imaging techniques were utilized to evaluate the fusion rate. Results The VAS-Back demonstrated a statistically significant improvement in Group UBE-TLIF compared to the other groups at the one-week postoperative evaluation ( p  < .05). Additionally, the UBE-TLIF group exhibited a significantly longer total operative time compared to the other groups ( p  < .05). However, it was noted that the Postop Hemovac drain were significantly greater in the MIS-TLIF and TLIF groups compared to the UBE-TLIF group ( p  < .05). Conclusions The present research demonstrated the effectiveness of UBE-TLIF, MIS-TLIF, and TLIF as surgical approaches for treating Meyerding grade I lumbar spondylolisthesis. Among these methods, UBE-TLIF demonstrated a reduction in Postop Hemovac drain, and an increase in operative duration.

Main conclusionSorghumBase provides a community portal that integrates genetic, genomic, and breeding resources for sorghum germplasm improvement.Public research and development in agriculture rely on proper data and resource sharing within stakeholder communities. For plant breeders, agronomists, molecular biologists, geneticists, and bioinformaticians, centralizing desirable data into a user-friendly hub for crop systems is essential for successful collaborations and breakthroughs in germplasm development. Here, we present the SorghumBase web portal (https://www.sorghumbase.org), a resource for the sorghum research community. SorghumBase hosts a wide range of sorghum genomic information in a modular framework, built with open-source software, to provide a sustainable platform. This initial release of SorghumBase includes: (1) five sorghum reference genome assemblies in a pan-genome browser; (2) genetic variant information for natural diversity panels and ethyl methanesulfonate (EMS)-induced mutant populations; (3) search interface and integrated views of various data types; (4) links supporting interconnectivity with other repositories including genebank, QTL, and gene expression databases; and (5) a content management system to support access to community news and training materials. SorghumBase offers sorghum investigators improved data collation and access that will facilitate the growth of a robust research community to support genomics-assisted breeding.

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.