bluuebunny/crossref_metadata_2025_split huggingface dataset:
De NovoVariants inMRTFBhave gain of function activity inDrosophilaand are associated with a novel neurodevelopmental phenotype with dysmorphic features
Authors: Andrews Jonathan C. , Mok Jung-Wan , Kanca Oguz , Jangam Sharayu , Tifft Cynthia , Macnamara Ellen F. , Russell Bianca , Wang Lee-kai ,   Undiagnosed Diseases Network, Nelson Stanley F. , Bellen Hugo J. , Yamamoto Shinya , Malicdan May , Wangler Michael F. 
Published: December 2022

AbstractMyocardin-Related Transcription Factor B (MRTFB) is an important transcriptional regulator which promotes the activity of an estimated 300 genes during different stages of development. Here we report two pediatric probands withde novovariants inMRTFB(R104G and A91P) and mild dysmorphic features, intellectual disability, global developmental delays, speech apraxia, and impulse control issues. As theMRTFBprotein is highly conserved between vertebrate and invertebrate model organisms, we generated a humanizedDrosophilamodel expressing the humanMRTFBprotein in the same spatial and temporal pattern as the fly gene. Expression of the humanMRTFBR104Gvariant using amrtf-T2A-GAL4line proved to be embryonic lethal. Additional phenotypes were also identified by expressing theMRTFBR104GandMRTFBA91Pvariant in a subset ofDrosophilatissues. Notably, expression within wing tissues resulted in an expansion of intervein tissue, wing vein thickening, shortening or loss of wing veins, and blistering. TheMRTFBR104GandMRTFBA91Pvariants also display a decreased level of actin binding within critical RPEL domains, resulting in increased transcriptional activity and changes in the organization of the Actin cytoskeleton. These changes were not observed in flies expressing two additional candidate variants,MRTFBN95SandMRTFBR109Q, highlighting that the location of the mutation within the 2nd RPEL domain is critical to the pathogenicity of the variant. These changes suggest that theMRTFBR104GandMRTFBA91Palleles we have identified affect the regulation of the protein and that these variants inMRTFBunderly a novel neurodevelopmental disorder.
Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors
Authors: Joisa Chinmaya U. , Chen Kevin A. , Berginski Matthew E. , Golitz Brian T. , Jenner Madison R. , Herrera Loeza Silvia G. , Yeh Jen Jen , Gomez Shawn M. 
Published: December 2022

AbstractProtein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Correspondingly, their central role in signaling implicates kinome dysfunction as a common driver of cancer, where numerous kinases have been identified as having a causal or modulating role in cancer development and progression. Driven by their importance, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Given the growing importance of kinase-targeted therapies, linking the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2of 0.7 and RMSE of 0.9), and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. Further, we validated these models experimentally by testing predicted effects in breast cancer cell lines, and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.
Whole slide image representation in bone marrow cytology
Authors: Mu Youqing , Tizhoosh H.R. , Dehkharghanian Taher , Campbell Clinton JV 
Published: December 2022

AbstractOne of the goals of AI-based computational pathology is to generate compact WSI representations, identifying the essential information required for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision support tools in hematology. We have previously published an end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSI. Using deep embeddings from this model, we construct bags of individual cell features from each WSI, and apply multiple instance learning to extract vector representations for each WSI. Using these representations in vector search, we achieved 0.58 ± 0.02 mAP@10 in WSI-level image retrieval, which outperforms the Random baseline (0.39 ± 0.1). Using a weighted k-nearest-neighbours (k-NN) model on these slide vectors, we predict five broad diagnostic labels on individual aspirate WSI with a weighted-macro-average F1 score of 0.57 ± 0.03 on the test set of 278 randomly sampled WSIs, which outperforms a classifier using empirical class prior probabilities (0.26 ± 0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.
Cell projection plots: a novel visualization of bone marrow aspirate cytology
Authors: Dehkharghanian Taher , Mu Youqing , Ross Catherine , Sur Monalisa , Tizhoosh H.R. , Campbell Clinton JV 
Published: December 2022

AbstractDeep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce cell projection plots (CPPs), which are novel, automatically generated visual summaries of bone marrow aspirate specimens. CPPs provide a compact summary of bone marrow aspirate cytology, and encompass relevant biological patterns such as neutrophil maturation. To gauge clinical relevance, CPPs were shown to three hematopathologists, who decided whether shown diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with 85% accuracy. Our finding suggests CPPs can compactly represent clinically relevant information from bone marrow aspirate specimens, and may be used to efficiently summarize bone marrow cytology to pathologists. CPP could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic support tool for digital pathology workflows.
Inhibition of LATS Kinases in Ovarian Cancer Activates Cyclin D1/CDK4 and Decreases DYRK1A Activity
Authors: Sesay Fatmata , Saini Siddharth , Pajimola Angel H , Witt Austin , Hu Bin , Koblinski Jennifer , Litovchick Larisa 
Published: December 2022

ABSTRACTThe controlled division of cells requires a coordination of multiple cellular pathways. Hippo pathway controls the organ size and restricts cell proliferation in response to the signals from cell surface receptors, and genetic alterations in the components of this pathway are common in cancer. LATS1 and LATS2 are homologous protein kinases that relay the signals from the environment to the Hippo effector YAP by direct phosphorylation that promotes its degradation. The genes encoding these kinases undergo frequent genetic losses in human cancers, with particularly high rates in high grade serous ovarian carcinoma (HGSOC), a highly lethal cancer with poorly understood mechanisms of pathogenesis and progression. We hypothesized that loss of LATS kinases could be a driver in this cancer and investigated signaling pathways downstream of LATS that could influence the ovarian cancer tumorigenic phenotypes. Depletion of both LATS1 and LATS2 was required to increase cell proliferation and disrupt the assembly of the cell-cycle regulatory DREAM complex. LATS-depleted human ovarian cancer cells formed bigger tumors in the immunocompromised mice, consistent with their tumor suppressor role. DREAM assembly depends on the activity of DYRK1A protein kinase, which was decreased in the LATS1/2-depleted cells. Furthermore, loss of LATS kinases increased the inhibitory phosphorylation of the retinoblastoma (Rb) family proteins, further promoting the DREAM disassembly that was rescued by CDK4 inhibitor palbociclib. Our study describes a crosstalk between the Hippo pathway and the cell cycle regulatory machinery converging on cyclin D1, a major regulator of the Rb tumor suppressor family, and highlights cellular pathways that could contribute to ovarian cancer pathogenesis and progression.
Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma
Authors: Liu He , Liu Meishuo , Tian Xibao , Wang Haina , Gao Jiujiao , Li Hanrui , Zhao Zhehuan , Liu Yu , Liu Caigang , Chen Xuan , Yang Yongliang 
Published: December 2022

AbstractBackgroundThe overactivation of NF-κB signaling is a key hallmark for the pathogenesis of extranodal natural killer/T cell lymphoma (ENKTL), a very aggressive subtype of non-Hodgkin’s lymphoma yet with rather limited control strategies. Previously, we found that the dysregulated exportin-1 (also known as CRM1) is mainly responsible for tumor cells to evade apoptosis and promote tumor-associated pathways such as NF-κB signaling.MethodsHerein we reported the discovery and biological evaluation of a potent small molecule CRM1 inhibitor, LFS-1107. We validated that CRM1 is a major cellular target of LFS-1107 by biolayer interferometry assay (BLI) and the knockdown of CRM1 conferred tumor cells with resistance to LFS-1107.ResultsWe found that LFS-1107 can strongly suppresses the growth of ENKTL cells at low-range nanomolar concentration yet with minimal effects on human platelets and healthy peripheral blood mononuclear cells. Treatment of ENKTL cells with LFS-1107 resulted in the nuclear retention of IkBαand consequent strong suppression of NF-κB transcriptional activities, NF-κB target genes downregulation and attenuated tumor cell growth and proliferation. Furthermore, LFS-1107 exhibited potent activities when administered to immunodeficient mice engrafted with human ENKTL cells.ConclusionsTherefore, LFS-1107 holds great promise for the treatment of ENKTL and may warrant translation for use in clinical trials.
Farmer’s perspectives of rodent pests’ damage and management practices in Wenchi highlands, central Ethiopia
Authors: Legese Kabeta , Bekele Afework 
Published: December 2022

AbstractA study was conducted to obtain information about the knowledge, attitudes and practices (KAP) of farmers in Wenchi highlands on rodent damage and their management practices. Farmers (n=395) from four highland villages of Wenchi District were randomly selected and interviewed using a semi-structured questionnaire. Rodents were identified as major pests, and perceived negatively among farmers. There were significant variations in the type of damage (χ2=112.698, df= 3, p < 0.05) and crop types susceptibility to rodent pest attack (χ2= 143.26, df = 3, P < 0.05). Crop damage (38.7%) and damage to human properties (27.9%) were the two dominant rodent related problems in the area. Barley was the most susceptible crop to rodent attack (57.5%). The occurrence of frequency of rodent pests and crop damage between the cropping stages also varied significantly. Most damage on barley crop (42.5%) occurred during the maturation stage. Farmers assessed and detected rodents damage by observing damaged seeds, damaged stores and rodent droppings in the storage, and stem cut of standing crops in the crop fields. None of the farmers have employed any management strategy in barley crop fields stating that this is practically impossible. In storage, farmers mainly use cats (53.73%) and trapping (22.64%) to control rodents. Detailed off-field rodent damage assessment, and community education for rodent management are recommended.
Is niche divergence more likely in parapatry? A test inSclerurus mexicanussensu lato (Aves: Furnariidae)
Authors: Cooper Jacob C. , Barragán Diego 
Published: December 2022

ABSTRACTAimWhile species’ niches are understood to diverge through time, the mode of these divergences is not fully understood. The null hypothesis of niche divergence in the present is that of conservatism; that species are likely to occur in the same environment as their ancestors and, therefore, as allopatric sister species. Alternatively, species are expected to diverge when selective pressure, such as parapatry with a congener, occurs. Here, we analyse niche divergence in a mosaic of allo- and parapatricSclerurus mexicanussensu lato populations to determine if niche divergence is more likely in parapatry.LocationNeotropics.MethodsWe created a dataset of 1,100 vetted localities forS. mexicanussensu lato, and assigned each point to each of the seven described populations. We created individual dispersal areas for each species in Maxent following: 1) a broad approach in which training areas where allowed to overlap with parapatric congeners; and 2) a narrow approach in which species dispersal areas adhered to strict parapatry and there was no overlap in training areas. We complemented these ENMs with ‘random’ models drawn from points within each training area, thus creating ‘null’ ENMs against which we could test niche divergence using the metric Schoener’s D.ResultsThere was no significant difference in the performance of broad and narrow training areas. Few significant divergences were found, and all of those that were found consisted of allopatric populations. Partial divergences were frequently recovered when comparing parapatric taxa.Main conclusionsIn contrast to our expectation, we found no significant evidence for increased niche divergence in parapatric versus allopatric species. Possible explanations for these findings include true conservatism even among parapatric lineages (e.g., additional environmental or biological factors may delimit distributions) or artefactual errors inherent to model creation. We discuss the implications of these findings, and discuss ways to improve upon tests of niche divergence in the future, especially when dealing with cases of parapatry.
The Role of 1-O-Acylceramide NP in Structural Organization and Permeability of the Stratum Corneum Lipid Matrix
Authors: Yang Moon Young , Lee Eun Ok , Park Chang Seo , Nam Yoon Sung 
Published: December 2022

ABSTRACTThe extracellular lipid matrix in the stratum corneum (SC) is crucial for generating a skin barrier (permeability) function. The lipid matrix contains three major components; ceramides, cholesterol, and free fatty acids. The broad diversity of ceramides depends on their molecular structures (e.g., hydroxylations and chain lengths) and plays a critical role in maintaining the structural integrity of the lipid matrix. Although recent studies identified a new subclass of ceramide, 1-O-acylceramide NP (CerENP), its precise role in the lipid matrix of SC is still elusive. Herein, we investigate the role of CerENP on the structure and permeability of the SC by molecular dynamics simulations. Our results suggest that the CerENP molecules induce a denser lipid matrix in the lateral dimension in the long periodicity phase model with a bilayer-slab- bilayer structure. Moreover, ethanol permeability analysis indicates that CerENP can suppress molecular permeability through the lipid matrix. This study provides insight into the role of a new subclass of ceramide in the SC, which can lead to our better understanding of skin organization and disease-related barrier dysfunction.
pH - responsive, reversible A-motif based DNA hydrogels: synthesis and biosensing applications
Authors: Morya Vinod , Shukla Ashish Kumar , Ghoroi Chinmay , Bhatia Dhiraj 
Published: December 2022

AbstractFunctional DNA hydrogels using various motifs and functional groups require perfect sequence designing to avoid cross-bonding interference with self or other structural sequences. The present work reports an A-motif functional DNA hydrogel that does not require any sequence design. A-motif DNA is a non-canonical parallel DNA duplex structure comprises homopolymeric deoxyadenosines (poly-dA) strands that undergo conformational changes from single strands at neutral pH to a parallel duplex DNA helix at acidic pH. Despite many advantages over other DNA motifs like no sequence, design is required and no cross-bonding interference with other structural sequences, A-motif has not been explored much. We successfully synthesized DNA hydrogel utilizing A-motif as a reversible handle to polymerize DNA three-way junction (3WJ). The composed A-motif hydrogel was first characterized by EMSA, & DLS, which shows the formation of higher-order structures. Further, we utilized imaging techniques like atomic force microscopy (AFM) and scanning electron microscope (SEM) validating its hydrogel like highly branched morphology. pH-induced conformational transformation from monomers to gel is quick and reversible, and was analysed for multiple acid-base cycles. The sol-to-gel transitions and gelation properties is further examined using rheological studies. The use of A-motif hydrogel in the visual detection of pathogenic target nucleic acid sequence is demonstrated for the first time using the capillary assay. Moreover, the pH-induced hydrogel formation is observedin-situas a layer over the mammalian cells. The proposed A-motif DNA scaffold has enormous potential in designing stimuli-responsive nanostructures that can be utilized for many biological applications.
Baseline Acute Myeloid Leukemia Prognosis Models using Transcriptomic and Clinical Profiles by Studying the Impacts of Dimensionality Reductions and Gene Signatures on Cox-Proportional Hazard
Authors: Sauvé Léonard , Hébert Josée , Sauvageau Guy , Lemieux Sébastien 
Published: December 2022

AbstractGene marker extraction to evaluate risk in cancer can refine the diagnosis process and lead to adapted therapies and better survival. These survival analyses can be done through computer systems and Machine Learning (ML) algorithms such as the Cox-Proportional-Hazard model from gene expression (GE) RNA-Seq data. However, optimal tuning of CPH from genome-wide GE data is challenging and poorly assessed so far. In this work we propose to interrogate an Acute Myeloid Leukemia (AML) dataset (Leucegene) to derive key components of the CPH driving down its performance and discovering its sensitivity to various factors in hoping to ameliorate the system. In this study, we compare the projection and selection data reduction techniques, mainly the PCA and LSC17 gene signature in combination with the CPH in a ML framework. Results reveals that CPH performs better with a combination of clinical and genetic expression features. We determine that projections performs better than selections without clinical information. We ascertain that CPH is affected by overfitting and that this overfitting is linked to the number and the content of input covariables. We show that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. We postulate that projection are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene. We extrapolate that these findings apply in the more general context of risk detection via machine learning in cancer. We see that higher capacity models such as CPH-DNNs systems can be improved via survival-derived projections and combat overfitting through heavy regularization.Author summaryThis study aims to investigate the feasibility of using gene expression to evaluate risk in cancer, and to compare the projection and selection data reduction techniques. The study used the Leucegene dataset to compare the PCA method and a previously published 17 genes signature in combination with the Cox-Proportional-Hazard model in a machine learning framework. Results showed that CPH was affected by overfitting and that this overfitting was linked to the number and the content of input covariables. The study found that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. The study concluded that projections are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene and can be tuned to improve their performance.Data availability statementSource code for pipelines and algorithms, as well as gene expression matrices, are available here:https://github.com/lemieux-lab/dimensions_coxph. Access to the Leucegene cohort’s survival times can be granted upon request and following ethical review.
Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques
Authors: Khojasteh Hakimeh , Pirgazi Jamshid 
Published: December 2022

AbstractPredicting drug-target interaction (DTI) is an important research area in the field of drug discovery. It means identifying the interaction between chemical compounds and protein targets. Wet lab experiments to explore these interactions are expensive as well as time-consuming. On the contrary, a dry lab environment focusing more on computational methods of interaction prediction can be helpful to limit the search space for the wet lab experiments and give clues before developing a new medicine. This paper proposes a novel drug-target interaction prediction method called SRX-DTI. First, we extract various descriptors from protein sequences, and the drug is encoded as FP2 fingerprint. Besides, we present the One-SVM-US technique to deal with imbalanced data. We also developed the FFS-RF algorithm, a forward feature selection algorithm, and coupled it with a random forest (RF) classifier to maximize the predictive performance. This feature selection algorithm removes the irrelevant features to obtain the best optimal features. Finally, the balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves significantly higher performance than other existing methods in predicting DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves significantly higher performance than other existing methods in predicting DTIs. The datasets and source code are available at:https://github.com/Khojasteh-hb/SRX-DTI.