bluuebunny/crossref_metadata_2025_split huggingface dataset:
Morphotype-Resolved Characterization of Microalgal Communities in a Nutrient Recovery Process with ARTiMiS Flow Imaging Microscopy
Authors: Gincley Benjamin , Khan Farhan , Alam Md Mahbubul , Hartnett Elaine , Kim Ga-Yeong , Molitor Hannah R. , Fisher Autumn , Bradley Ian , Guest Jeremy , Pinto Ameet J. 
Published: January 2025

AbstractMicroalgae-driven nutrient recovery represents a promising technology to reduce effluent phosphorus while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based deep neural network (DNN) models. Across a two-year study period,Scenedesmusspp. appeared most important for phosphorus removal, which were negatively associated with elevated temperatures and nitrite/nitrate concentrations.ChlorellaandMonoraphidiumalso played an important role in system performance. For bothScenedesmusandChlorella, smaller morphological types were more often associated with high system performance, whereas larger morphotypes implied a stress response correlating with poor phosphorus recovery rates. These results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.Graphical Abstract
Engineered hydrogel biomaterials facilitate lung progenitor cell differentiation from induced pluripotent stem cells
Authors: Tanneberger Alicia E. , Blomberg Rachel , Bilousova Ganna , Ryan Amy L. , Magin Chelsea M. 
Published: January 2025

ABSTRACTLung progenitor (LP) cells identified by the expression of transcription factor NK2 homeobox 1 (NKX2.1) are essential for development of all lung epithelial cell types and hold tremendous potential for pulmonary research and translational regenerative medicine applications. Here we present engineered hydrogels as a promising alternative to the naturally derived materials that are often used to differentiate human induced pluripotent stem cells (iPSCs) into LP cells. Poly(ethylene glycol) norbornene (PEGNB) hydrogels with defined composition were used to systematically investigate the role of microenvironmental stiffness, cell origin, and splitting during the differentiation process. Results demonstrated each factor impacted LP differentiation efficiency. Soft hydrogels replicating healthy lung stiffness (Elastic modulus (E), E = 4.00 ± 0.25 kPa) produced the highest proportion of LP cells (54% by flow cytometry), stiff hydrogels (E = 18.83 ± 2.24 kPa) resulted in 48% differentiation efficiency, and a thin coating of Matrigel on tissue culture (TC) plastic (E∼3 GPa) resulted in the lowest proportion of LP cells (32%) at the end of the non-split differentiation protocol. Collectively these results showed that engineered hydrogels enabled control over parameters that impacted differentiation and produced LP cells using well-defined microenvironments that may improve our ability to translate iPSC-derived LP cells into clinical applications.NEW & NOTEWORTHYStandard iPSC differentiation protocols rely on Matrigel, a basement membrane extract from mouse sarcoma cells that is poorly defined and exhibits significant batch-to-batch variation. Due to these limitations Matrigel-derived products have never been approved by the Food and Drug Administration. This study introduces a novel method for differentiating iPSCs into lung progenitor cells using well-defined hydrogel substrates. These biomaterials not only enhance differentiation efficiency, but also streamline the regulatory pathway, facilitating their potential therapeutic application.
Unlocking the genomic repertoire of a cultivated megaphage
Authors: Buchan Andra , Wiedman Stephanie , Lambirth Kevin , Bellanger-Perry Madeline , Figueroa Jose L. , Wright Elena T. , Suresh Patil Shivprasad , Zhang Qibin , Thomas Julie A. , Serwer Philip , White Richard Allen 
Published: January 2025

AbstractMegaphages are bacteriophages (i.e., phages) with exceptionally large genomes that are ecosystem cosmopolitans, infect various bacterial hosts, and have been discovered across various metagenomic datasets globally. To date, almost all megaphages have evaded cultivation, with only phage G being in active culture for over 50 years. We examined with multiomics this five decades long cultivated history from nine different laboratories with five different lab variants to the modern era. In this work, we resolved the five complete phage G genomes, the particle proteome,de novomethylome, and used artificial intelligence (AI) to annotate the genome of phage G. Phage G is one of the largest phages with a size of >0.6 µm, about half the width of the host cell, and a 499 kbp, non-permuted, linear genome that has, uniquely among known phages, two pairs of ends. Its closest known relative is Moose phage W30-1 which was metagenomically assembled without cultivation from a moose rumen sample. Phage G has >650 protein-coding open reading frames (ORFs), with >65% being hypothetical proteins with no known function, with the rest of the genome geared towards nucleic acid replication (e.g., helicases, polymerases, endonucleases) and are structural in nature (e.g., capsid, tail, portal, terminase). The genome encodes a 35 kbp stretch with 66 ORFs without any known functional homology, a cryptic genomic region that is roughly the size of phage lambda. Phage G has an expansive repertoire of auxiliary metabolic genes (AMGs) acquired from its bacterial host, including aphoH,ftsZ,UvsX/RecA-like, gyrA, gyrB,andDHFR. Furthermore, AMGs discovered in phage G could manipulate host sporulation (sspD, RsfA, spoK) and antiviral escape genes (e.g., anti-CBass nuclease and Anti-Pycsar protein). Phage proteomics found >15% of the protein ORFs were present in either the wild-type or mutant variants of phage G, including genes involved in replication (e.g.,UvsX/RecA-like), host sporulation, as well as structural genes (e.g., capsid, tail, portal). The methylome of phage G was localized to the cryptic region with limited functional homology, with supervised machine learning (i.e., HMMs) was unable to resolve this region, but was resolved with protein structural AI. This cryptic region was a hot spot for methylation at 32%, where many of the functions of the ORF are still unknown. Our study represents a doorway into the complexity of the genomic repertoire of the only cultivated megaphage, highlighting five decades of continuous cultivation for the first time.
Deciphering T-wave Morphologies on ECGs: The Simplified Egg and Changing Yolk Model and the Importance of the QTp Interval
Authors: Stone Kieran , Mistry Arvind , Cyrus Daniel , Cannon John , Mokrzecki Ignacy , Rezwan Faisal I 
Published: January 2025

AbstractThe Electrocardiogram (ECG) serves as an integral tool in the diagnosis and management of a variety of cardiac diseases. It visualises electrical activity in the heart, offering insights into several cardiac processes, including ventricular repolarisation. The morphology of the T-wave observed on ECGs during this repolarisation phase varies and can be peaked, flat, inverted, or biphasic, each representing different cardiac conditions. Despite their prevalence, the interpretation of these patterns remains challenging. Therefore, we proposed the Simplified Egg and Changing Yolk Model, a novel idea to aid in the understanding of these T-wave morphologies in ECGs.The proposed Simplified Egg and Changing Yolk Model was developed through an analysis of various T-wave morphologies and their corresponding clinical implications. The model was further designed to conceptualise the ST interval and the T-wave as a single unit, contributing to a simplified yet comprehensive understanding of ventricular repolarisation. In this context, the ‘Q-wave start to T-wave peak interval’(QTp) was compared to the more commonly used ‘corrected QT-interval’ (QTc) for assessing the risk of arrhythmia and the effects of medication that prolong the QT-interval.The Simplified Egg and Changing Yolk Model could effectively explain and interpret the variation of ECG patterns associated with ventricular repolarisation. It provided insight into the relevance of deflections seen during this phase. Importantly, the model identified QTp as a more reliable measure than QTc for assessing arrhythmia risk and evaluating medication impacts on the QT-interval.Our model offers a significant enhancement to the understanding of ventricular repolarisation and its manifestation on ECGs. By emphasising the superiority of QTp over QTc in clinical assessment, this model can have significant impact in clinical practice.
Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis
Authors: Lohner Valerie , Badhwar Amanpreet , Detcheverry Flavie E. , García Cindy L. , Gellersen Helena M. , Khodakarami Zahra , Lattmann René , Li Rui , Low Audrey , Mazo Claudia , Metz Amelie , Parent Olivier , Phillips Veronica , Saeed Usman , Tan Sean YW , Tamburin Stefano , Llewellyn David J. , Rittman Timothy , Waters Sheena , Bernal Jose 
Published: January 2025

ABSTRACTIntroductionMachine learning (ML) algorithms using neuroimaging markers of cerebral small vessel disease (CSVD) are a promising approach for classifying cognitive impairment and dementia.MethodsWe systematically reviewed and meta-analysed studies that leveraged CSVD features for ML-based diagnosis and/or prognosis of cognitive impairment and dementia.ResultsWe identified 75 relevant studies: 43 on diagnosis, 27 on prognosis, and 5 on both. CSVD markers are becoming important in ML-based classifications of neurodegenerative diseases, mainly Alzheimer’s dementia, with nearly 60% of studies published in the last two years. Regression and support vector machine techniques were more common than other approaches such as ensemble and deep-learning algorithms. ML-based classification performed well for both Alzheimer’s dementia (AUC 0.88 [95%-CI 0.85–0.92]) and cognitive impairment (AUC 0.84 [95%-CI 0.74–0.95]). Of 75 studies, only 16 were suitable for meta-analysis, only 11 used multiple datasets for training and validation, and six lacked clear definitions of diagnostic criteria.DiscussionML-based models using CSVD neuroimaging markers perform well in classifying cognitive impairment and dementia. However, challenges in inconsistent reporting, limited generalisability, and potential biases hinder adoption. Our targeted recommendations provide a roadmap to accelerate the integration of ML into clinical practice.
Delirium and High Sedation Levels are Common in Critically Ill Patients with COVID-19 and Associated with Poor Long-Term Outcomes
Authors: Bucklin Abigail A. , Ganglberger Wolfgang , Tesh Ryan A. , Quadri Syed A. , Ayub Muhammad Abubakar , Maher Susan S. , Montoya Miguel Patino , Malik Preeti , Alabsi Haitham S. , Rosand Jonathan , Kimchi Eyal Y. , Akeju Oluwaseun , Mukerji Shibani S. , Wiener-Kronish Jeanine , Westover M. Brandon 
Published: January 2025

AbstractBackgroundWe investigated delirium prevalence and potential effects of long-term sedation in critically ill COVID-19 patients; to identify opportunities for improving sedation practices and delirium prevention.MethodsThis prospective, single-center, observational cohort study was conducted from April-June 2020. Adult COVID-19 patients were eligible if admitted to an ICU with mechanical ventilation/intravenous sedation; or a general care unit with brain monitoring due to altered mental status. Patients were evaluated daily until discharge using the Richmond Agitation-Sedation Scale, Confusion Assessment Method for the ICU, and CAM-Severity. Cumulative doses of sedation and paralytic medications were recorded. At three months post-enrollment, cognition, mood, and quality of life were measured by the Telephone Interview for Cognitive Status (TICS), Center for Epidemiologic Studies Depression Scale 10-item (CES-10), and EuroQol 5-Dimension-3 Level (EQ-5D-3L), respectively.Results67 patients were enrolled, with a mean (SD) age of 59 (12) years, 30 (45%) Hispanic, 43 (64%) developing acute respiratory distress syndrome, 55 (82%) mechanically ventilated (mean duration of 22.9 days), and 5 comatose for the entire study. Of the 62 patients assessed for delirium, 61 (98%) had delirium at least once, with a mean (SD) of 12.7 (13.0) days. >90% of patients received opioids, benzodiazepines, or propofol at least once; median (IQR) total dose of 37.4 (78.9) mg (fentanyl equivalents), 52.5 (813.3) mg (midazolam equivalents), and 46 (53) g (propofol), respectively. At follow-up, 40 (60%) patients were reached, while 16 (24%) were deceased/comfort measures. Patients showed reductions in cognition, mood, and quality of life with median (IQR) scores for TICS (0-41): 30 (26-33); CES-D-10 (0-30): 6 (4-12); EQ-5D-3L (1-3): 2 (mobility, self-care, usual activities, pain/discomfort).ConclusionCritically and acutely ill patients with COVID-19 early in the pandemic experienced a high rate of delirium and sedation. Large doses of sedatives may contribute to greater delirium burden during hospitalization, and lead to poor clinical outcomes.
Intramuscular adipose tissue physically restricts functional muscle recovery
Authors: Norris A.M. , Palzkill V.R. , Appu A.B. , Ryan T.E. , Kopinke D. 
Published: January 2025

SUMMARYWith age and disease, skeletal muscle is progressively lost and replaced by fibrotic scar and intramuscular adipose tissue (IMAT). While strongly correlated, it remains unclear whether IMAT has a functional impact on muscle. In the present study, we evaluated the effects of IMAT during muscle injury by creating a mouse model where the cellular origin of IMAT, fibro/adipogenic progenitors (FAPs), are prevented from differentiating into adipocytes (FATBLOCK model). We found that blocking IMAT after an adipogenic injury allowed muscle to regenerate more efficiently, resulting in enhanced function. Our data explain why acute muscle injuries featuring IMAT infiltration, such as rotator cuff tears and acute denervation injuries, exhibit poor regeneration and lead to a loss in muscle function. It also demonstrates the therapeutic importance of preventing IMAT formation in acute injuries in order to maximize regeneration and minimize loss in muscle mass and function.
Forage preference in two geographically co-occurring fungus gardening ants: a dietary DNA approach
Authors: Richards-Perhatch Matthew , Boshers Elizabeth , Greenwold Matthew , Seal Jon 
Published: January 2025

AbstractTraditional methods of forage identification are impractical to use with non-leafcutting fungus gardening ants, making diet-related ecological and life history questions difficult to study. In an attempt to address this limitation, we utilized dietary DNA metabarcoding as a method for directly identifying foraged substrate from the ant’s fungus gardens. We then attempted to use metrics based off of these dietary DNA results to determine if two co-occurring fungus gardening ant species were niche separated by their preference in forage material. Fungus gardens from the co-occurring ant speciesTrachymyrmex septentrionalisandMycetomoellerius turrifexwere collected from a plot in East Texas and sequenced using chloroplasttrnLand mitochondriaCOIprimers. Results of this analysis indicate that dietary DNA metabarcoding was successful in identifying and describing a diversity of plant material present in the fungus gardens, with a total of 42 plant taxa identified. When fungal substrate plant taxa alpha and beta diversity metrics were compared between colonies ofT. septentrionalisandM. turrifex, no evidence of diet-based niche separation was detected. Overall, our results show that dietary DNA techniques represent a promising tool that can be used to identify foraged plant material in ant fungus gardens, enabling the study of future diet-based ecological and natural history questions.
Pathway-based clustering identifies two subtypes of cancer-associated fibroblasts associated with distinct molecular and clinical features in pancreatic ductal carcinoma
Authors: Ai Hongjing , Nie Rongfang , Wang Xiaosheng 
Published: January 2025

AbstractExisting single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms single-cell gene expression matrix into pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to PDAC scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggest a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.
cryoTIGER: Deep-Learning Based Tilt Interpolation Generator for Enhanced Reconstruction in Cryo Electron Tomography
Authors: Majtner Tomáš , Kreysing Jan Philipp , Tuijtel Maarten W. , Cruz-León Sergio , Liu Jiasui , Hummer Gerhard , Beck Martin , Turoňová Beata 
Published: January 2025

ABSTRACTCryo-electron tomography enables the visualization of macromolecular complexes within native cellular environments, but is limited by incomplete angular sampling and the maximal electron dose that biological specimen can be exposed to. Here, we developed cryoTIGER (Tilt Interpolation Generator for Enhanced Reconstruction), a computational workflow leveraging a deep learning-based frame interpolation to generate intermediate tilt images. By interpolating between tilt series projections, cryoTIGER improves angular sampling, leading to enhanced 3D reconstructions, more refined particle localization, and improved segmentation of cellular structures. We evaluated our interpolation workflow on diverse datasets and compared its performance against non-interpolated data. Our results demonstrate that deep learning-based interpolation improves image quality and structural recovery. The presented cryoTIGER framework offers a computational alternative to denser sampling during tilt series acquisition, paving the way for enhanced cryo-ET workflows and advancing structural biology research.
Neuronal expression of E2F4DN restores adult neurogenesis in homozygous 5xFAD mice via TrkB signaling
Authors: Lozano-Ureña Anna , Llabrés-Mas Aina Maria , Ramón-Landreau Morgan , Fraj-Cebrián Alberto , Sánchez-Puelles Cristina , Garrido-García Alberto , Cano-Daganzo Vanesa , Valdés-Lora Lorena , Frade José María 
Published: January 2025

AbstractThe etiology of Alzheimer’s disease (AD) has been associated with impaired neurogenesis in the adult subventricular zone (SVZ), but the molecular mechanism leading to this impairment remains poorly understood. Neuronal dysfunction in the AD-affected brain might lead to reduced production of neuron-derived paracrine factors acting through receptors necessary for adult SVZ neurogenesis (ASN). To test this hypothesis, we focused on the TrkB receptor, which can transduce signals from the neurotrophins BDNF and NT4/5, since TrkB is known to regulate the ASN process and its function becomes altered in AD. Here we show that ASN is impaired in the SVZ of homozygous 5xFAD (h5xFAD) mice. This impairment is prevented by administering an AAV.PHP.eB vector that expresses in neurons the transcription factor E2F4 carrying the Thr249Ala/Th251Ala mutation (E2F4DN), a gene therapeutic approach previously demonstrated to exert multifactorial effects in this mouse model of AD. The use of culture media conditioned by primary cortical neurons expressing E2F4DN was able to recover the proliferative and differentiative capacity of neural stem cells (NSCs) isolated from h5xFAD mice. This effect was blocked by inhibiting the TrkB receptor. Accordingly, TrkB activation mimicked the effect of the E2F4DN-conditioned medium on the proliferative and differentiative capacity of h5xFAD NSCs, a finding consistent with the upregulation of NT4/5 expression in the E2F4DN-transduced neurons. We conclude that the activation of TrkB by neurotrophins released by E2F4DN-expressing neurons can recover the ASN phenotype in 5xFAD mice. Therefore, the multifactorial therapeutic capacity of E2F4DN includes the recovery of impaired ASN through the upregulation of TrkB signaling in NSCs.