bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Small Pixel Effect in Ultra-High-Resolution Photon-Counting CT of the Lumbar Spine
Authors: Huflage Henner , Hendel Robin , Woznicki Piotr , Conrads Nora , Feldle Philipp , Patzer Theresa Sophie , Ergün Süleyman , Bley Thorsten Alexander , Kunz Andreas Steven , Grunz Jan-Peter 
Published: February 2024

Objectives
Image acquisition in ultra-high-resolution (UHR) scan mode does not impose a dose penalty in photon-counting CT (PCCT). This study aims to investigate the dose saving potential of using UHR instead of standard-resolution PCCT for lumbar spine imaging.

Materials and Methods
Eight cadaveric specimens were examined with 7 dose levels (5–35 mGy) each in UHR (120 × 0.2 mm) and standard-resolution acquisition mode (144 × 0.4 mm) on a first-generation PCCT scanner. The UHR images were reconstructed with 3 dedicated bone kernels (Br68 [spatial frequency at 10% of the modulation transfer function 14.5 line pairs/cm], Br76 [21.0], and Br84 [27.9]), standard-resolution images with Br68 and Br76. Using automatic segmentation, contrast-to-noise ratios (CNRs) were established for lumbar vertebrae and psoas muscle tissue. In addition, image quality was assessed subjectively by 19 independent readers (15 radiologists, 4 surgeons) using a browser-based forced choice comparison tool totaling 16,974 performed pairwise tests. Pearson's correlation coefficient (r) was used to analyze the relationship between CNR and subjective image quality rankings, and Kendall W was calculated to assess interrater agreement.

Results
Irrespective of radiation exposure level, CNR was higher in UHR datasets than in standard-resolution images postprocessed with the same reconstruction parameters. The use of sharper convolution kernels entailed lower CNR but higher subjective image quality depending on radiation dose. Subjective assessment revealed high interrater agreement (W = 0.86; P < 0.001) with UHR images being preferred by readers in the majority of comparisons on each dose level. Substantial correlation was ascertained between CNR and the subjective image quality ranking (all r's ≥ 0.95; P < 0.001)

Conclusions
In PCCT of the lumbar spine, UHR mode's smaller pixel size facilitates a considerable CNR increase over standard-resolution imaging, which can either be used for dose reduction or higher spatial resolution depending on the selected convolution kernel.
Daily vocalization patterns of the Saipan Reed Warbler (Acrocephalus hiwae)
Authors: Gaul Willson , Lin Jie , Roark Ellie 
Published: February 2024

ABSTRACTWe investigated how detectability and vocalization patterns of Saipan Reed Warblers (Acrocephalus hiwae) varied by time of day. We used long-duration sound recordings from eleven locations occupied by Saipan Reed Warblers to model the probability of detecting a vocalization in each hour of the day. We found that Saipan Reed Warblers sang during all daylight hours. We did not find evidence of a dawn or evening chorus in this species. These results are useful for determining what time of day surveys of Saipan Reed Warblers should be conducted, which is particularly relevant because Saipan Reed Warblers are protected by local and U.S. Federal laws.
Plant-on-a-chip: continuous, soilless electrochemical monitoring of salt uptake and tolerance among different genotypes of tomato
Authors: Coatsworth Philip , Cotur Yasin , Asfour Tarek , Zhou Zihao , Flauzino José M. R. , Bozkurt Tolga , Güder Firat 
Published: February 2024

AbstractTomatoes (Solanum lycopersicum), a high-value crop, exhibit a unique relationship with salt, where increased levels of NaCl can enhance flavor, aroma and nutritional quality but can cause oxidative damage and reduce yields. A drive for larger, better-looking tomatoes has reduced the importance of salt sensitivity, a concern considering that the sodium content of agricultural land is increasing over time. Currently, there are no simple ways of comparing salt tolerance between plants, where a holistic approach looking at [Na+] throughout the plant typically involves destructive, single time point measurements or expensive imaging techniques. Finding methods that collect rapid information in real time could improve the understanding of salt resistance in the field. Here we investigate the uptake of NaCl by tomatoes using TETRIS (Time-resolvedElectrochemicalTechnology for plantRoot environmentIn-situchemicalSensing), a platform used to measure chemical signals in the root area of living plants. Low-cost, screen-printed electrochemical sensors were used to measure changes in salt concentrationviaelectrical impedance measurements, facilitating the monitoring of the uptake of ions by roots. We not only demonstrated differences in the rate of uptake of NaCl between tomato seedlings under different growth conditions, but also showed differences in uptake between varieties of tomato with different NaCl sensitivities and the relatively salt-resistant “wild tomato” (Solanum pimpinellifolium) sister species. Our results suggest that TETRIS could be used to ascertain physiological traits of salt resistance found in adult plants but at the seedling stage of growth. This extrapolation, and the possibility to multiplex and change sensor configuration, could enable high-throughput screening of many hundreds or thousands of mutants or varieties.
AAclust:k-optimized clustering for selecting redundancy-reduced sets of amino acid scales
Authors: Breimann Stephan , Frishman Dmitrij 
Published: February 2024

AbstractSummaryAmino acid scales are crucial for sequence-based protein prediction tasks, yet no gold standard scale set or simple scale selection methods exist. We developed AAclust, a wrapper for clustering models that require a pre-defined number of clustersk, such ask-means. AAclust obtains redundancy-reduced scale sets by clustering and selecting one representative scale per cluster, wherekcan either be optimized by AAclust or defined by the user. The utility of AAclust scale selections was assessed by applying machine learning models to 24 protein benchmark datasets. We found that top-performing scale sets were different for each benchmark dataset and significantly outperformed scale sets used in previous studies. Notably, model performance showed a strong positive correlation with the scale set size. AAclust enables a systematic optimization of scale-based feature engineering in machine learning applications.Availability and implementationThe AAclust algorithm is part of AAanalysis, a Python-based framework for interpretable sequence-based protein prediction, which will be made freely accessible in a forthcoming publication.ContactStephan Breimann (Stephan.Breimann@dzne.de) and Dmitrij Frishman (dimitri.frischmann@tum.de)Supplementary informationFurther details on methods and results are provided in Supplementary Material.
C. elegansRIG-I-like receptor DRH-1 signals via CARDs to activate anti-viral immunity in intestinal cells
Authors: Batachari Lakshmi E. , Dai Alyssa Y. , Troemel Emily R. 
Published: February 2024

AbstractUpon sensing viral RNA, mammalian RIG-I-like receptors activate downstream signals using caspase activation and recruitment domains (CARDs), which ultimately promote transcriptional immune responses that have been well-studied. In contrast, the downstream signaling mechanisms for invertebrate RIG-I-like receptors are much less clear. For example, theCaenorhabditis elegansRIG-I-like receptor DRH-1 lacks annotated CARDs and upregulates the distinct output of RNA interference (RNAi). Here we found that, similar to mammal RIG-I-like receptors, DRH-1 signals through two tandem caspase activation and recruitment domains (2CARD) to induce a transcriptional immune response. Expression of DRH-1(2CARD) alone in the intestine was sufficient to induce immune gene expression, increase viral resistance, and promote thermotolerance, a phenotype previously associated with immune activation. We also found that DRH-1 is required in the intestine to induce immune gene expression, and we demonstrate subcellular colocalization of DRH-1 puncta with double-stranded RNA inside the cytoplasm of intestinal cells upon viral infection. Altogether, our results reveal mechanistic and spatial insights into anti-viral signaling inC. elegans,highlighting unexpected parallels in RIG-I-like receptor signaling betweenC. elegansand mammals.SignificanceViruses are ubiquitous pathogens that challenge diverse organisms, from bacteria to killer whales. While anti-viral defense has been well-studied in mammals, less is known about defense in invertebrates, including the roundwormCaenorhabditis elegans. Here we show that theC. elegansviral sensor DRH-1 shares similarities to a viral sensor in mammals called RIG-I. We find that DRH-1 has a signaling motif resembling the 2CARD motif, which is found in RIG-I and activates anti-viral immunity. We demonstrate that overexpression of DRH-1(2CARD) inC. eleganspromotes resistance to viral infection, and that DRH-1 forms clusters inside intestinal cells during viral infection, similar to RIG-I in humans. Overall, these findings provide insights intoC. elegansanti-viral immunity, highlighting similarities with mammalian anti-viral immunity.
Biotic interactions promote local adaptation to soil in plants
Authors: Dorey Thomas , Frachon Léa , Rieseberg Loren H. , Kreiner Julia , Schiestl Florian P. 
Published: February 2024

AbstractAlthough different ecological factors shape adaptative evolution in natural habitats, we know little about how their interactions impact local adaptation. Here we used eight generations of experimental evolution with outcrossingBrassica rapaplants as a model system, in eight treatment groups that varied in soil type, herbivory (with/without aphids), and pollination mode (hand- or bumblebee-pollination), to study how biotic interactions affect local adaptation to soil. First, we show that several plant traits evolved in response to biotic interactions in a soil-specific way. Second, using a reciprocal transplant experiment, we demonstrate that significant local adaptation to soil-type evolved in the “number of open flowers”, a trait used as a fitness proxy, but only in plants that evolved with herbivory and bee pollination. Whole genome re-sequencing of experimental lines revealed that biotic interactions caused a 10-fold increase in the number of SNPs across the genome with significant allele frequency change, and that alleles with opposite allele frequency change in different soil types (antagonistic pleiotropy) were most common in plants with an evolutionary history of herbivory and bee pollination. Our results demonstrate that the interaction of mutualists and antagonists can facilitate local adaptation to soil type through antagonistic pleiotropy.
Spatial transcriptomic profiling of human retinoblastoma
Authors: Wang Luozixian , Hung Sandy , Urrutia-Cabrera Daniel , Kong Roy C. K. , Staffieri Sandra , Ludlow Louise E. , Lau Xianzhong , Wang Peng-Yuan , Hewitt Alex W. , Wong Raymond C.B. 
Published: February 2024

AbstractRetinoblastoma (RB) represents one of the most prevalent intraocular cancers in children. Understanding the tumor heterogeneity in RB is important to design better targeted therapies. Here we used spatial transcriptomic to profile human retina and RB tumor to comprehensively dissect the spatial cell-cell communication networks. We found high intratumoral heterogeneity in RB, consisting of 10 transcriptionally distinct subpopulations with varying levels of proliferation capacity. Our results uncovered a complex architecture of the tumor microenvironment that predominantly consisted of cone precursors, as well as glial cells and cancer-associated fibroblasts. We delineated the cell trajectory underlying malignant progression of RB, and identified key signaling pathways driving genetic regulation across RB progression. We also explored the signaling pathways mediating cell-cell communications in RB subpopulations, and mapped the spatial networks of RB subpopulations and region neighbors. Altogether, we constructed the first spatial gene atlas for RB, which allowed us to characterize the transcriptomic landscape in spatially-resolved RB subpopulations, providing novel insights into the complex spatial communications involved in RB progression.
Fast and accurate modeling and design of antibody-antigen complex using tFold
Authors: Wu Fandi , Zhao Yu , Wu Jiaxiang , Jiang Biaobin , He Bing , Huang Longkai , Qin Chenchen , Yang Fan , Huang Ningqiao , Xiao Yang , Wang Rubo , Jia Huaxian , Rong Yu , Liu Yuyi , Lai Houtim , Xu Tingyang , Liu Wei , Zhao Peilin , Yao Jianhua 
Published: February 2024

AbstractAccurate prediction of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Currently, structure prediction for protein monomers has achieved considerable success, and promising progress has been made in extending this achievement to the prediction of protein complexes. However, despite these advancements, fast and accurate prediction of antibody-antigen complex structures remains a challenging and unresolved issue. Existing end-to-end prediction methods, which rely on homology and templates, exhibit sub-optimal accuracy due to the absence of co-evolutionary constraints. Meanwhile, conventional docking-based methods face difficulties in identifying the contact interface between the antigen and antibody and require known structures of individual components as inputs. In this study, we present a fully end-to-end approach for three-dimensional (3D) atomic-level structure predictions of antibodies and antibody-antigen complexes, referred to as tFold-Ab and tFold-Ag, respectively. tFold leverages a large protein language model to extract both intra-chain and inter-chain residue-residue contact information, as well as evolutionary relationships, avoiding the time-consuming multiple sequence alignment (MSA) search. Combined with specially designed modules such as the AI-driven flexible docking module, it achieves superior performance and significantly enhanced speed in predicting both antibody (1.6% RMSD reduction in the CDR-H3 region, thousand times faster) and antibody-antigen complex structures (37% increase in DockQ score, over 10 times faster), compared to AlphaFold-Multimer. Given the performance and speed advantages, we further extend the capability of tFold for structure-based virtual screening of binding antibodies, as well as de novo co-design of both structure and sequence for therapeutic antibodies. The experiment results demonstrate the potential of tFold as a high-throughput tool to enhance processes involved in these tasks. To facilitate public access, we release code and offer a web service for antibody and antigen-antibody complex structure prediction, which is available athttps://drug.ai.tencent.com/en.
Distinguishable topological properties of functional genome networks in HIV-1 reservoirs
Authors: Wiśniewski Janusz , Więcek Kamil , Ali Haider , Pyrc Krzysztof , Kula-Păcurar Anna , Wagner Marek , Chen Heng-Chang 
Published: February 2024

AbstractHIV-1 reservoirs display heterogeneous nature, lodging both intact and defective proviruses. Recent evidence has shed light on their difference, particularly in the context of immune-mediated selection. To deepen our understanding of such heterogeneous HIV-1 reservoirs and their functional implications, we pioneered the integration of basic concepts of graph theory to characterize the composition of HIV-1 reservoirs. Our analysis revealed noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, played a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthened the accuracy of classification between elite controllers and ART-treated patients. Overall, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV-1 reservoirs.
Evaluating the likelihood for areas important for conservation to be recognized as Other Effective area-based Conservation Measures
Authors: Cook Carly N , Rao Madhu , Clyne Peter J , Rathbone Vanessa , Barrientos Christian , Boveda Antonio , Diment Alex , Parra Jorge , Falabella Valeria , Linkie Matthew , Kujirakwinja Deo , Ostrowski Stephane , Olson Kirk , Patankar Vardhan , Rasolofomanan Lovy , Grantham Hedley S 
Published: February 2024

ABSTRACTOther effective area-based conservation measures (OECMs) have expanded area-based conservation to recognize sites that deliver effective biodiversity outcomes even if not managed for conservation. Yet our ability to identify sites likely to qualify as OECMs remains limited. To address this gap, we established and tested a set of indicators to judge whether sites meet the essential criteria to be considered OECMs, evaluating a large, global sample of 173 important conservation areas: 81 potential OECMs and 92 nearby protected areas. We found that most potential OECMs were largely in good condition with the potential to achieve conservation outcomes, but none currently met all the OECM criteria. Formally designated protected areas in our dataset performed better but the majority also failed the criteria. With so many important conservation areas unable to deliver effective conservation outcomes, our findings raise important questions about how to ensure area-based conservation promotes positive and sustained outcomes for biodiversity.
Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms
Authors: Steinberg Philippa L. , Liu Lydia Y. , Neiman-Golden Anna , Patel Yash , Boutros Paul C. 
Published: February 2024

AbstractBackgroundIntra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS.ResultsWe characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm.ConclusionsThese findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.
Deciphering Endothelial and Mesenchymal Organ Specification in Vascularized Lung and Intestinal Organoids
Authors: Miao Yifei , Tan Cheng , Pek Nicole M. , Yu Zhiyun , Iwasawa Kentaro , Kechele Daniel O. , Sundaram Nambirajan , Pastrana-Gomez Victor , Kishimoto Keishi , Yang Min-Chi , Jiang Cheng , Tchieu Jason , Whitsett Jeffrey A. , McCracken Kyle W. , Rottier Robbert J. , Kotton Darrell N. , Helmrath Michael A. , Wells James M. , Takebe Takanori , Zorn Aaron M. , Chen Ya-Wen , Guo Minzhe , Gu Mingxia 
Published: February 2024

SummaryTo investigate the co-development of vasculature, mesenchyme, and epithelium crucial for organogenesis and the acquisition of organ-specific characteristics, we constructed a human pluripotent stem cell-derived organoid system comprising lung or intestinal epithelium surrounded by organotypic mesenchyme and vasculature. We demonstrated the pivotal role of co-differentiating mesoderm and endoderm via precise BMP regulation in generating multilineage organoids and gut tube patterning. Single-cell RNA-seq analysis revealed organ specificity in endothelium and mesenchyme, and uncovered key ligands driving endothelial specification in the lung (e.g., WNT2B and Semaphorins) or intestine (e.g., GDF15). Upon transplantation under the kidney capsule in mice, these organoids further matured and developed perfusable human-specific sub-epithelial capillaries. Additionally, our model recapitulated the abnormal endothelial-epithelial crosstalk in patients withFOXF1deletion or mutations. Multilineage organoids provide a unique platform to study developmental cues guiding endothelial and mesenchymal cell fate determination, and investigate intricate cell-cell communications in human organogenesis and disease.HighlightsBMP signaling fine-tunes the co-differentiation of mesoderm and endoderm.The cellular composition in multilineage organoids resembles that of human fetal organs.Mesenchyme and endothelium co-developed within the organoids adopt organ-specific characteristics.Multilineage organoids recapitulate abnormal endothelial-epithelial crosstalk in FOXF1-associated disorders.
Explaining Conformational Diversity in Protein Families through Molecular Motions
Authors: Lombard Valentin , Grudinin Sergei , Laine Elodie 
Published: February 2024

ABSTRACTProteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. This study introduces Dimensionality Analysis for protein Conformational Exploration (DANCE) for a systematic and comprehensive description of protein families conformational variability. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. This work improves our understanding of how proteins deform to perform their functions and opens ways to a standardised evaluation of methods designed to sample and generate protein conformations.