bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning
Authors: Horvath Jakub , Jedlicka Pavel , Kratka Marie , Kubat Zdenek , Kejnovsky Eduard , Lexa Matej 
Published: June 2024

AbstractBackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key compounds, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs.ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting - GBC), (ii) hybrid CNN-LSTM models, and (iii) a pre-trained transformer-based model (DNABERT) using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the CNN-LSTM hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. SHAP positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box, and TG..CA patterns around both LTR edges.ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’-edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.
TLCellClassifier: Machine Learning Based Cell Classification for Bright-Field Time-Lapse Images
Authors: Jiang Qibing , Sudalagunta Praneeth Reddy , Meads Mark , Zhao Xiaohong , Achille Alexandra , Noyes David , Silva Maria , Canevarolo Rafael Renatino , Shain Ken , Silva Ariosto , Zhang Wei 
Published: June 2024

AbstractImmunotherapies have shown promising results in treating patients with hematological malignancies like multiple myeloma, which is an incurable but treatable bone marrow-resident plasma cell cancer. Choosing the most efficacious treatment for a patient remains a challenge in such cancers. However, pre-clinical assays involving patient-derived tumor cells co-cultured in anex vivoreconstruction of immune-tumor micro-environment have gained considerable notoriety over the past decade. Such assays can characterize a patient’s response to several therapeutic agents including immunotherapies in a high-throughput manner, where bright-field images of tumor (target) cells interacting with effector cells (T cells, Natural Killer (NK) cells, and macrophages) are captured once every 30 minutes for upto six days. Cell detection, tracking, and classification of thousands of cells of two or more types in each frame is bound to test the limits of some of the most advanced computer vision tools developed to date and requires a specialized approach. We propose TLCellClassifier (time-lapse cell classifier) for live cell detection, cell tracking, and cell type classification, with enhanced accuracy and efficiency obtained by integrating convolutional neural networks (CNN), metric learning, and long short-term memory (LSTM) networks, respectively. State-of-the-art computer vision software like KTH-SE and YOLOv8 are compared with TLCellClassifier, which shows improved accuracy in detection (CNN) and tracking (metric learning). A two-stage LSTM-based cell type classification method is implemented to distinguish between multiple myeloma (tumor/target) cells and macrophages/monocytes (immune/effector cells). Validation of cell type classification was done both using synthetic datasets andex vivoexperiments involving patient-derived tumor/immune cells.Availability and implementationhttps://github.com/QibingJiang/cell classification ml
Durable Antimicrobial Microstructure Surface (DAMS) Enabled by 3D-Printing and ZnO Nanoflowers
Authors: Yuqing FNU , Zhang Shuhuan , Peng Ruonan , Silva Justin , Ernst Olivia , Lapizco-Encinas Blanca H , Liu Rui , Du Ke 
Published: June 2024

A.AbstractNumerous studies have been trying to create nanomaterials based antimicrobial surfaces to combat the growing bacterial infection problems. Mechanical durability has become one of the major challenges to applying those surfaces in real life. In this study, we demonstrate the Durable Antimicrobial Microstructures Surface (DAMS) consisting of DLP 3D printed microstructures and zinc oxide (ZnO) nanoflowers. The microstructures serve as a protection armor for the nanoflowers during abrasion. The antimicrobial ability was tested by immersing in 2E8 CFU/mLEscherichia coli(E. coli) suspension and then evaluated using electron microscopy. Compared to the bare control, our results show that the DAMS reduces bacterial coverage by more than 90% after 12 hrs of incubation and approximately 50% after 48 hrs of incubation before abrasion. Importantly, bacterial coverage is reduced by approximately 50% after 2 min of abrasion with a tribometer, and DAMS remains effective even after 6 min of abrasion. These findings highlight the potential of DAMS as an affordable, scalable, and durable antimicrobial surface for various biomedical applications.
Drug combinations with apoptosis pathway targeted agents alrizomadlin, pelcitoclax, and dasminapant in multi-cell type tumor spheroids
Authors: Coussens Nathan P. , Dexheimer Thomas S. , Silvers Thomas , Sanchez Phillip R. , Takebe Naoko , Doroshow James H. , Teicher Beverly A. 
Published: June 2024

ABSTRACTApoptosis, or programmed cell death, plays a critical role in maintaining tissue homeostasis by eliminating damaged or abnormal cells. Dysregulation of apoptosis pathways is a hallmark of cancer, allowing malignant cells to evade cell death and proliferate uncontrollably. Targeting apoptosis pathways has emerged as a promising therapeutic strategy in cancer treatment, aiming to restore the balance between cell survival and death. In this context, the MDM2 inhibitor alrizomadlin, the Bcl-2/Bcl-xL inhibitor pelcitoclax, and the IAP family inhibitor dasminapant were evaluated both individually and in combination with standard of care and investigational anticancer small molecules with a spheroid model of solid tumors. The multi-cell type tumor spheroids were grown from endothelial cells and mesenchymal stem cells combined with human malignant cells that were either established or patient-derived cell lines from the NCI Patient- Derived Models Repository. The malignant cell lines were derived from a range of solid tumors including uterine carcinosarcoma, synovial sarcoma, rhabdomyosarcoma, soft tissue sarcoma, malignant fibrous histiocytoma, malignant peripheral nerve sheath tumor (MPNST), pancreas, ovary, colon, breast, and small cell lung cancer. Interactions were observed from combinations of the apoptosis pathway targeted agents. Additionally, interactions were observed from combinations of the apoptosis pathway targeted agents with other agents, including PARP inhibitors, the XPO1 inhibitor eltanexor, and the PI3K inhibitor copanlisib. Enhanced activity was also observed from combinations of the apoptosis pathway targeted agents with MAPK pathway targeted agents, including the MEK inhibitor cobimetinib as well as adagrasib and MRTX1133, which specifically target the KRAS G12C and G12D variants, respectively.SIGNIFICANCEMulti-cell type tumor spheroids grown from normal and patient-derived malignant cell lines were screened to identify potentially efficacious combinations with the investigational agents alrizomadlin, pelcitoclax or dasminapant that target cell death pathways. This article highlights novel combinations with FDA approved drugs including eltanexor, cobimetinib and copanlisib.
Zinc-starved Brassicaceae Plants Secrete Peptides that Induce Root Expansion
Authors: Niehs Sarah P. , Rajniak Jakub , Johnson Anna , Wengier Diego L. , Sattely Elizabeth S. 
Published: June 2024

AbstractZinc (Zn) deficiency is recognized as a global crisis as it is observed in half of all agricultural soils. However, the molecular mechanisms that drive plant physiological responses to soil Zn deficiency are not well understood. We used an untargeted metabolomics approach to search for metabolites exuded from roots during Zn deficiency stress, which led to the discovery of a collection of secreted small defensin-like peptides inArabidopsis thaliana(named Zinc-Deficiency Responsive Peptides (ZDRPs)). Phylogenetic analysis and untargeted metabolomics revealed ZDRPs in at least eleven accessions ofA. thalianaand nine members of the Brassicaceae family. Analysis ofArabidopsisgene mutants and overexpressing lines, in combination with chemical complementation experiments, unveiled a critical role of these peptides in plant root growth. We hypothesize that Brassicaceae secreted peptides enable plants to expand their root mass to reach Zn-rich soil layers and optimize Zn uptake. These data reveal a critical relationship between plant survival, Zn status, root morphology and peptide production. Taken together, our results expand our knowledge regarding micronutrient deficiency responses in plants and could enable in engineering approaches to make plants more resilient to low Zn conditions.SignificanceZinc deficiency is the most abundant micronutrient deficiency affecting about 50% of arable lands thus presenting a high burden for plant health and agriculture globally. In this study, we reveal a metabolic strategy by Brassicaceae to deal with low Zn concentrations. We characterize the role of peptides expressed upon zinc deficiency in a variety of important crop plants. The discovery of a cryptic class of peptides that are made by plant roots specifically suffering from Zn deficiency provides critical insight into the molecular mechanisms by which plants dynamically acclimate to nutrient-limited soils. The identification of peptides actively secreted by zinc-deprived plants has translational value for sustainable agriculture, human health, and bioengineering approaches to enable tolerance to low zinc.
Perinatal brain group 3 innate lymphoid cells are involved in the formation of murine dural lymphatics
Authors: del Rio Serrato Alba , Hernández Borja Latorre , Bartl Evie , Stehle Christina , Hölsken Oliver , Diefenbach Andreas , Romagnani Chiara , Infante-Duarte Carmen 
Published: June 2024

SummaryThe central nervous system (CNS) contains a pool of innate lymphoid cells (ILCs) of unclear composition and functionality and unknown origin. Here, we demonstrate that group 1 ILCs (inc. ex-ILC3s) and ILC2s are resident cells with low proliferative capacities and subtype specific CNS compartmentalization. We show for the first time that CNS ILC seeding and niche establishment occurs during early life and is initiated by both, ILC progenitors-like PLFZ+PD- 1+cells and lineage committed ILCs. While group 1 ILCs and RORγt+ILC3s were found within the embryonic and postnatal brain, ILC2s reached the CNS after birth and were predominantly localized within the dura mater, proving early regional distribution. Interestingly, RORγt+ILC3s were only detected perinatally and vanished from the CNS as an outcome of decreased turnover andin situILC3-to-ILC1 conversion. Remarkably, we showed that perinatal RORγt+ILC3s are required for the correct development of the lymphatic vessels within the dura.
Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks
Authors: Dominguez Almela Victoria , Croker Abigail R , Stafford Richard 
Published: June 2024

AbstractPredictive models are often complex to produce and interpret, yet can offer valuable insights for management, conservation and policy-making through relatively simple approaches. We demonstrate that by using straightforward concepts to describe interactions between model components, predictive models can be effectively constructed using basic spreadsheet tools. Using a new R package (BBNet), these models can be analysed, visualised, and sensitivity tested to assess how information flows through the system’s components. The models are based on Bayesian belief networks (BBN) but adapted to overcome some of the complexity and shortcomings of the traditional BBN approach. The models are not fully quantitative, but outcomes between different modelled scenarios can be considered ordinally (i.e. ranked). Parameterisation of models can also be through data, literature, expert opinion, or questionnaires and surveys of opinion. While we have focussed on the use of the models in environmental and ecological problems (including with links to management and social outcomes), their application does not need to be restricted to these disciplines, and use in financial systems, molecular biology, political sciences and many other disciplines are possible.
Label-Free Optical Mapping for Large-Area Biomechanical Dynamics of Multicellular Systems
Authors: Lin Yen-Ju , Tan Xing Haw Marvin , Wang Yijie , Chung Pei-Shan , Zhang Xiang , Wu Ting-Hsiang , Wu Tung-Yu , Deb Arjun , Chiou Pei-Yu 
Published: June 2024

AbstractMapping cellular activities over large areas is crucial for understanding the collective behaviors of multicellular systems. Biomechanical properties, such as cellular traction force, serve as critical regulators of physiological states and molecular configurations. However, existing technologies for mapping large-area biomechanical dynamics are limited by the small field of view and scanning nature. To address this, we propose a novel platform that utilizes a vast number of optical diffractive elements for mapping large-area biomechanical dynamics. This platform achieves a field-of-view of 10.6 mm X 10.6 mm, a three-orders-of-magnitude improvement over traditional traction force microscopy. Transient mechanical waves generated by monolayer neonatal rat ventricular myocytes were captured with high spatiotemporal resolution (130 fps and 20 µm for temporal and spatial resolution, respectively). Furthermore, its label-free nature allows for long-term observations extended to a week, with minimal disruption of cellular functions. Finally, simultaneous measurements of calcium ions concentrations and biomechanical dynamics are demonstrated.
Performance evaluation of adaptive introgression classification methods
Authors: Romieu Jules , Camarata Ghislain , Crochet Pierre-André , de Navascués Miguel , Leblois Raphaël , Rousset François 
Published: June 2024

AbstractIntrogression, the incorporation of foreign variants through hybridization and repeated backcross, is increasingly being studied for its potential evolutionary consequences, one of which is adaptive introgression (AI). In recent years, several statistical methods have been proposed for the detection of loci that have undergone adaptive introgression. Most of these methods have been tested and developed to infer the presence of Neanderthal or Denisovan AI in humans. Currently, the behaviour of these methods when faced with genomic datasets from evolutionary scenarios other than the human lineage remains unknown. This study therefore focuses on testing the performance of the methods using test data sets simulated under various evolutionary scenarios inspired by the evolutionary history of human, wall lizard (Podarcis) and bear (Ursus) lineages. These lineages were chosen to represent different combinations of divergence and migration times. We study the impact of these parameters, as well as migration rate, population size, selection coefficient and presence of recombination hotspots, on the performance of three methods (VolcanoFinder, Genomatnn and MaLAdapt) and a standalone summary statistic (Q95(w,y)). Furthermore, the hitchhiking effect of an adaptively introgressed mutation can have a strong impact on the flanking regions, and therefore on the discrimination between the genomic windows classes (i.e.AI/non-AI). For this reason, three different types of non-AI windows are taken into account in our analyses: independently simulated neutral introgression windows, windows adjacent to the window under AI, and windows coming from a second neutral chromosome unlinked to the chromosome under AI. Our results highlight the importance of taking into account adjacent windows in the training data in order to correctly identify the window with the mutation under AI. Finally, our tests show that methods based on Q95 seem to be the most efficient for an exploratory study of AI.
B4 Raf-like MAPKKK RAF24 regulatesArabidopsis thalianaflowering time through HISTONE MONO-UBIQUITINATION 2
Authors: Li Qiaomu , Wang Le , Rodriguez Gallo Maria Camila , Mehta Devang , Scandola Sabine , Talasila Mohana , Uhrig RG 
Published: June 2024

ABSTRACTThe timing of flowering is a critical agronomic trait governed by a number of external cues. Despite our genetic understanding of flowering time being well established, we have a limited understanding of how these signals are transmitted to different flowering genes through protein phosphorylation. Here, we characterize a novel B4 Raf-like MAPKKK protein kinase called RAF24, whose mutation results in an early flowering phenotype. Comparative analysis to related B4 Raf-like MAPKKKs indicates that RAF24 unqiuely affects flowering time, while phosphoproteome analyses found RAF24 impacts the phosphorylation status of proteins involved in distinct flowering pathways. In particular, we found the RING-type ubiquitin ligase HISTONE MONO-UBIQUITINATION 2 (HUB2) to possess the largest phosphorylation change inraf24deficient plants relative to wild-type Arabidopsis and that RAF24 suppresses ligase activity of HUB2 in order to maintain approriate levels of H2Bub1. Furthermore, we found that RAF24 regulates HUB2 phosphorylation through subclass I and III SUCROSE NON-FERMENTING KINASE 2 (SnRK2) protein kinases; known substrates of B4 RAF-like MAPKKKs. Lastly, using a combination of phospho-mimetic and -ablative plant lines, we validate the importance of HUB2 phosphorylation at S314in regulating flowering time. Collectively, our findings implicate RAF24 as a higher-order flowering regulator, while further implicating HUB2 as a centerpiece of flowering regulation.
LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency
Authors: Zaragoza Michael V. , Bui Thuy-Anh , Widyastuti Halida P. , Mehrabi Mehrsa , Cang Zixuan , Sha Yutong , Grosberg Anna , Nie Qing 
Published: June 2024

ABSTRACTLMNA-Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms ofLMNA-Related DCM using induced pluripotent stem cells derived from a family with a heterozygousLMNA c.357-2A>Gsplice-site mutation. We differentiated oneLMNAmutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10ZNFgenes and RNA polymerase II transcription in Pluripotent cells (PP);BMP4and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis,CDH2and EMT in CM;LMNAand epigenetic regulation andDDIT4and mTORC1 signaling in EPDC. Top DEG also included:XISTand other X-linked genes, six imprinted genes:SNRPN,PWAR6,NDN,PEG10,MEG3,MEG8, and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in otherLMNAdisease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.
Unveiling the Power of PROTAC Valency: Navigating Cell Type-Specific Hook Effects
Authors: Chitsazan Arash , Eisele Frederik , Rabieifar Parisa , Park Hyunsoo , Nordberg Markus , Liu Jianming , Geschwindner Stefan , Dahl Göran 
Published: June 2024

AbstractTargeted protein degradation (TPD) using bivalent proteolysis-targeting chimeras (PROTAC) technology has shown potential in expanding the “druggable” proteome. In their publication inNature Chemical Biology, Imaide et al.1posited that augmenting PROTAC valency could potentially lead to the formation of long-lived ternary complexes between PROTAC, the protein of interest (POI), and E3 ligase, thereby constraining the formation of potent binary complexes, as evidenced by a pronounced hook effect. The authors introduced SIM1, a trivalent von Hippel–Lindau (VHL)-based PROTAC, which exhibited a superior degradation profile in comparison to its parent molecule MZ1, towards bromo and extra terminal (BET) proteins, with a predilection for BRD2. The authors attributed this heightened degradation capability of SIM1 over bivalent MZ1 as supportive evidence for their hypothesis. While we concur with the notion that increasing valency and avidity could enhance the efficacy of a PROTAC, the claim that trivalent PROTACs unequivocally eliminate the hook effect is not entirely accurate. We propose that the presence or absence of a hook effect is influenced by numerous factors beyond PROTAC valency.