bluuebunny/crossref_metadata_2025_split huggingface dataset:
I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data
Authors: Dang Tung , Fuji Yushiro , Kumaishi Kie , Usui Erika , Kobori Shungo , Sato Takumi , Toda Yusuke , Sakurai Kengo , Yamasaki Yuji , Tsujimoto Hisashi , Hirai Masami Yokota , Ichihashi Yasunori , Iwata Hiroyoshi 
Published: August 2023

AbstractHigh-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities’ interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics microbiome data has been proposed. In this study, we propose a novel framework called integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data. The I-SVVS approach addresses a specific Bayesian mixture model for each type of omics data, such as an infinite Dirichlet multinomial mixture model for microbiome data and an infinite Gaussian mixture model for metabolomic data. This approach is expected to reduce the computational time of the clustering process and improve the accuracy of the clustering results. Additionally, I-SVVS identifies a critical set of representative variables in multi-omics microbiome data. Three datasets from soybean, mice, and humans (each set integrated microbiome and metabolome) were used to demonstrate the potential of I-SVVS. The results indicate that I-SVVS achieved improved accuracy and faster computation compared to existing methods across all test datasets. It effectively identified key microbiome species and metabolites characterizing each cluster. For instance, the computational analysis of soybean dataset, including 377 samples with 16,943 microbiome species and 265 metabolome features, was completed in 2.18 hours using I-SVVS, compared to 2.35 days with Clusternomics and 1.12 days with iClusterPlus. The software for this analysis, written in Python, is freely available athttps://github.com/tungtokyo1108/I-SVVS.
mosaicMPI: a framework for modular data integration across cohorts and -omics modalities
Authors: Verhey Theodore B. , Seo Heewon , Gillmor Aaron , Thoppey-Manoharan Varsha , Schriemer David , Morrissy Sorana 
Published: August 2023

AbstractAdvances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available athttps://github.com/MorrissyLab/mosaicMPI.
Classifying the risk for myasthenic crisis using data-driven explainable machine learning with informative feature design and variance control – a pilot study
Authors: Bershan Sivan , Meisel Andreas , Mergenthaler Philipp 
Published: August 2023

AbstractImportanceMyasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring intensive care treatment and invasive therapies. Classifying patients at high-risk for MC facilitates treatment decisions and helps prevent disease progression.ObjectiveTo test whether machine learning models trained with real-world routine clinical data can aid precisely identifying MG patients at risk for MC.DesignThis is a pseudo-prospective cohort study of MG patients presenting since January 2010.SettingSingle center.ParticipantsA cohort of 51 MG patients was used for model training based on a defined set of real-world clinical data. The cohort was created from a convenience sample of 13 MC patients matched based on sex, five-year age band, antibody status, thymus pathology with MG patients who had not suffered an MC. Data analyses and model refinements were performed from June 2022 to May 2023.ExposureClassification of MG patients to high or low risk for MC using Lasso regression or random forest machine learning models.Main Outcomes and MeasuresThe accuracy of the risk classification was assessed by patient.ResultsThis study included 51 MG patients (13 MC, 38 non-MC; median age MC group 70.5, non-MC group 65.5). The mean cross-validated AUC classifying MG patients as high or low risk for MC based on simple or compound features derived from real-world routine clinical data showed a predictive accuracy of 68.8% for the regularized Lasso regression and of 76.5% for the random forest model. Feature importance scores suggest that multimorbidity may play a role in risk classification. Different thresholds were applied to tune model performance to optimal parameters. Studying result stability across 100 runs further indicated that the random forest model was better suited to cope with feature variance. Studying feature importance across 5100 model runs identified explainable features to distinguish MG patients at high or low risk for MC.Conclusions and RelevanceIn this study, feasibility of classifying risk for MC based on real-world routine clinical data using machine learning was shown. The models showed accurate and consistent performance indicating the utility of personalized risk assessment in MG patients using machine learning models.Key PointsQuestionCan machine learning models be used to classify Myasthenia gravis patients into groups at high or low risk for myasthenic crisis with high precision based on explainable data-driven features derived from real-world clinical data?FindingsIn this pseudo-prospective study of 51 Myasthenia gravis patients, the risk of myasthenic crisis using real-world clinical data was accurately classified employing two machine learning models with explainable features.MeaningThese findings suggest that it is possible to classify the risk for myasthenic crisis in patients based on real-world clinical data with high precision.
Designed Endocytosis-Triggering Proteins mediate Targeted Degradation
Authors: Huang Buwei , Abedi Mohamad , Ahn Green , Coventry Brian , Sappington Isaac , Wang Rong , Schlichthaerle Thomas , Zhang Jason Z. , Wang Yujia , Goreshnik Inna , Chiu Ching Wen , Chazin-Gray Adam , Chan Sidney , Gerben Stacey , Murray Analisa , Wang Shunzhi , O’Neill Jason , Yeh Ronald , Misquith Ayesha , Wolf Anitra , Tomasovic Luke M. , Piraner Dan I , Gonzalez Maria J. Duran , Bennett Nathaniel R. , Venkatesh Preetham , Satoe Danny , Ahlrichs Maggie , Dobbins Craig , Yang Wei , Wang Xinru , Vafeados Dionne , Mout Rubul , Shivaei Shirin , Cao Longxing , Carter Lauren , Stewart Lance , Spangler Jamie B. , Bernardes Gonçalo J.L. , Roybal Kole T. , Greisen Per , Li Xiaochun , Bertozzi Carolyn , Baker David 
Published: August 2023

AbstractEndocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.
One-pot chemo-enzymatic synthesis and one-step recovery of homogeneous long-chain polyphosphates from microalgal biomass
Authors: Lin Yi-Hsuan , Nishikawa Shota , Jia Tony Z. , Yeh Fang-I , Khusnutdinova Anna , Yakunin Alexander F. , Fujishima Kosuke , Wang Po-Hsiang 
Published: August 2023

SummaryAbstract FigureGraphical abstractPhosphate, an essential component of life, fertilizers, and detergents, is a finite resource that could be depleted within 70 years, while improper phosphate waste disposal in aquatic environments results in eutrophication. Despite some chemical-based methods, biological phosphorus removal using polyphosphate-accumulating organisms, such as microalgae, serves as a sustainable alternative to reclaim phosphate from wastewater. Polyphosphates have profound biological functions and biomedical applications, serving as energy stock, coagulation factors, and antiviral agents depending on their length, showing inherent value in polyphosphate recovery. Here, we leveraged the power of thermodynamic coupling and phase transitions to establish a one-pot, two-step multi-enzyme cascade to convert polydisperse polyphosphate in microalgae biomass into high-molecular-weight insoluble long-chain polyphosphates, allowing for one-step purification. We then optimzed a thermo-digestion approach to transform the 1,300-mers into shorter polyphosphates. Altogether, the processes established here enable the establishment of a sustainable P bioeconomy platform to refine microalgal biomass for biotechnological uses.
Cyclic stretch regulates epithelial cell migration in a frequency dependent manner via vinculin recruitment to cell-cell contacts
Authors: Dow Liam P. , Surace Stacey , Morozov Katrene , Kennedy Reagan , Pruitt Beth L. 
Published: August 2023

AbstractEpithelial cell migration is critical in regulating wound healing and tissue development. The epithelial microenvironment is incredibly dynamic, subjected to mechanical cues including cyclic stretch. While cyclic cell stretching platforms have revealed responses of the epithelium such as cell reorientation and gap formation, few studies have investigated the long-term effects of cyclic stretch on cell migration. We measured the migratory response of the epithelium to a range of physiologically relevant frequencies and stretch. We integrated our experimental approach with high-throughput cell segmentation to discover a relationship between changes in cell morphology and migration as a function of cyclic stretch. Our results indicate that lower stretch frequencies (i.e., 0.1 Hz) arrest epithelial migration, accompanied by cell reorientation and high cell shape solidity. We found that this response is also accompanied by increased recruitment of vinculin to cell-cell contacts, and this recruitment is necessary to arrest cell movements. This work demonstrates a critical role for frequency dependence in epithelial response to mechanical stretch. These results confirm the mechanosensitive nature of vinculin within the adherens junction, but independently reveal a novel mechanism of low frequency stress response in supporting epithelial integrity by arresting cell migration.
R2C2+UMI: Combining concatemeric consensus sequencing with unique molecular identifiers enables ultra-accurate sequencing of amplicons on Oxford Nanopore Technologies sequencers
Authors: Deng Dori Z.Q. , Verhage Jack , Neudorf Celine , Corbett-Detig Russell , Mekonen Honey , Castaldi Peter J. , Vollmers Christopher 
Published: August 2023

AbstractThe sequencing of PCR amplicons is a core application of high-throughput sequencing technology. Using unique molecular identifiers (UMIs), individual amplified molecules can be sequenced to very high accuracy on an Illumina sequencer. However, Illumina sequencers have limited read length and are therefore restricted to sequencing amplicons shorter than 600bp unless using inefficient synthetic long-read approaches. Native long-read sequencers from Pacific Biosciences and Oxford Nanopore Technologies can, using consensus read approaches, match or exceed Illumina quality while achieving much longer read lengths. Using a circularization-based concatemeric consensus sequencing approach (R2C2) paired with UMIs (R2C2+UMI) we show that we can sequence ∼550nt antibody heavy-chain (IGH) and ∼1500nt 16S amplicons at accuracies up to and exceeding Q50 (<1 error in 100,0000 sequenced bases), which exceeds accuracies of UMI-supported Illumina paired sequencing as well as synthetic long-read approaches.
Cerebro-spinal Flow Pattern in the Cervical Subarachnoid Space of Healthy Volunteers: Influence of the Spinal Cord morphology
Authors: Leblond Lugdivine , Sudres Patrice , Evin Morgane 
Published: August 2023

AbstractIntroductionThe cerebrospinal fluid is a fundamental part of the central nervous system. The objective of this study is to assess the variations in the CSF flow pattern associated with change in the morphology of the subarachnoidal space of the cervical canal of healthy humans by developing a Computational Fluid Dynamics model.Methods3D T2-space MRI sequence images of the cervical spine were used to segment 11 cervical subarachnoid space. Model validation (time-step, mesh size, size and number of boundary layers, influences of parted inflow and inflow continuous velocity) was performed a 40-year-old patient-specific model. Simulations were performed using CFD approach simulating transient flow (Sparlart-Almaras turbulence model) with a mesh size of 0.6, 6 boundary layers of 0.05 mm, a time step of 20 ms simulated on 15 cycles. Distributions of components velocity and WSS were respectively analyzed within the subrachachnoidal space (intervertebral et intravertebral levels) and on dura and pia maters.ResultsMean values of CSF velocity ranges between 0.07 and 0.17 m.s-1and 0.1 and 0.3 m.s-1for maximum values. Maximum Wall Shear Stress values vary between 0.1 and 0.5 Pa with higher value at the middle of the cervical spine on pia mater and at the lower part of the cervical spine on dura mater. Intra and inter-individual variations of the Wall Shear Stress were found to differ between individuals and significant correlation was found with compression ratio (r=0.76) and lower correlation with occupation ratio and cross section area of the spinal cord.ConclusionThis study is a first to present WSS and velocity distribution in the subarachnoidal canal of 11 individuals and to described the relationship between with subarachnoid canal morphology highlighting the influence of the shape of the spinal cord within the canal on the CSF flow pattern.
Mapping the Genetic Interaction Network of PARP inhibitor Response
Authors: Simpson Danny , Ling Jia , Jing Yangwode , Adamson Britt 
Published: August 2023

SUMMARYGenetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying that system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response. Evaluating genetic interactions at this scale, with and without exposure to PARPi, revealed hierarchical organization of the pathways and complexes that maintain genome stability during normal growth and defined changes that occur upon accumulation of DNA lesions due to cytotoxic doses of PARPi. We uncovered unexpected relationships among DNA repair genes, including context-specific buffering interactions between the minimally characterizedAUNIPand BRCA1-A complex genes. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.
Advancements in Managing Wound Biofilm: A Systematic Review and Meta-Analysis of Randomized Controlled Trials on Topical Modalities
Authors: Astrada Adam , Pamungkas Rian Adi , Biomed Khoirul Rista Abidin M. 
Published: August 2023

AbstractSignificanceGaining insights into the efficacy of topical wound treatments for biofilm eradication is pivotal to enhancing wound management strategies. Despite numerous available agents claiming anti-biofilm properties, the substantiating evidence remains inconclusive. This study aimed to assess the immediate impact of topical wound treatments on wound biofilm and healing outcomes in acute and chronic ulcers.Recent AdvancesWe comprehensively searched PubMed, ClinicalTrials.gov, and Google Scholar. Additionally, eligible grey literature was incorporated. English-language randomized controlled trials, observational, cohort, and case-control studies targeting biofilm prevention, inhibition, or elimination across diverse wound types were included. Primary outcomes included biofilm presence and elimination, supplemented by secondary outcomes encompassing reduced wound size, complete closure, and diminished infection indicators. Bacterial load reduction and biofilm presence were also assessed.Critical IssuesTwenty-eight articles met the inclusion criteria. Various modalities were identified, including biofilm-visualization techniques like wound blotting and handheld autofluorescence imaging. A notable high bias risk was observed across all studies. Pooled analysis for the primary outcomes was infeasible due to limited eligible studies and data reporting challenges. The primary source of bias is the lack of consensus on objectively evaluating the biofilm. As for the secondary outcomes, the pooled analysis for complete wound closure (6 RCTs, yielding n=439) and presence of infection/inflammation (2 RCTs, yielding n=284) showed no significant difference, with log odds ratio (LOD) 0.03 (95% CI: – 1.02 – 1.09; τ2=1.09, SE: 1.10, Q=14.33,p=.014) and LOD – 0.95 (95% CI: – 3.54 – 1.64; τ2=2.32, Q=2.71,p=.10), respectively.Future DirectionOur findings suggest insufficient evidence to support anti-biofilm claims of topical modalities. Clinicians’ skill appears to play a pivotal role in biofilm elimination and wound healing enhancement, with potential optimization through visual-guided techniques like wound blotting and autofluorescence imaging. More rigorous clinical trials are warranted to ascertain the efficacy of these techniques.
Configural behavior response to a two component odor mixture
Authors: O’Brien Rory F. , Klymyshyn Dmytro , Cohen Lawrence B. 
Published: August 2023

AbstractConfigural odors are mixtures of odorant molecules that are perceived as an odor distinct from the components. They provide an attractive target for investigations of odor perception in the olfactory bulb. We investigated the perception of octanal and citronellal in mice using a Go/No-Go behavioral paradigm, and show that mice trained on the mixture of the two odorants respond selectively to the mixture, but not to the components delivered separately.
Software Package for Transcranial Magnetic Stimulation Coil and Coil Array Analysis and Design
Authors: Morales Leah , Wartman William A , Ferreira Jonathan , Miles Alton , Daneshzand Mohammad , Lu Hanbing , Nummenmaa Aapo R. , Deng Zhi-De , Makaroff Sergey N. 
Published: August 2023

AbstractObjectiveThis study aims to describe a MATLAB software package for transcranial magnetic stimulation (TMS) coil analysis and design.ApproachElectric and magnetic fields of the coils as well as their self- and mutual (for coil arrays) inductances are computed, with or without a magnetic core. Solid and stranded (Litz wire) conductors are also taken into consideration. The starting point is the centerline of a coil conductor(s), which is a 3D curve defined by the user. Then, a wire mesh and a computer aided design (CAD) mesh for the volume conductor of a given cross-section (circular, elliptical, or rectangular) are automatically generated. Self- and mutual inductances of the coil(s) are computed. Given the conductor current and its time derivative, electric and magnetic fields of the coil(s) are determined anywhere in space.Computations are performed with the fast multipole method (FMM), which is the most efficient way to evaluate the fields of many elementary current elements (current dipoles) comprising the current carrying conductor at a large number of observation points. This is the major underlying mathematical operation behind both inductance and field calculations.Main ResultsThe wire-based approach enables precise replication of even the most complex physical conductor geometries, while the FMM acceleration quickly evaluates large quantities of elementary current filaments. Agreement to within 0.74% was obtained between the inductances computed by the FMM method and ANSYS Maxwell 3D for the same coil model. Although not provided in this study, it is possible to evaluate non-linear magnetic cores in addition to the linear core exemplified. An experimental comparison was carried out against a physical MagVenture C-B60 coil; the measured and simulated inductances differed by only 1.25%, and nearly perfect correlation was found between the measured and computed E-field values at each observation point.SignificanceThe developed software package is applicable to any quasistatic inductor design, not necessarily to the TMS coils only.