bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain-age prediction: a systematic comparison of machine learning workflows
Authors: More Shammi , Antonopoulos Georgios , Hoffstaedter Felix , Caspers Julian , Eickhoff Simon B. , Patil Kaustubh R. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: November 2022

AbstractThe difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer’s and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.HighlightsThere is an effect of both feature space and ML algorithm on prediction error.Voxel-wise features performed better than parcel-wise features.GPR, KRR and RVR algorithms performed well.The within-site and cross-site delta-behavior correlations disagree.Higher brain-age delta inference in AD depends on data used for bias correction.
KBPRNA: A novel method integrating bulk RNA-seq data and LINCS-L1000 gene signatures to predict kinase activity based on machine learning
Authors: Zhang Yuntian , Yao Lantian , Huang Yixian , Zhang Wenyang , Pang Yuxuan , Lee Tzongyi 
Published: November 2022

AbstractBackgroundKinases are a type of enzymes which can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates. Kinase activities could be utilized to be represented as specific biomarkers of specific cancer types. Nowadays novel algorithms have already been developed to compute kinase activities from phosphorylated proteomics data. However, phosphorylated proteomics sequencing could be costly expensive and need valuable samples. Moreover,not methods which could achieve kinase activities from bulk RNA-sequence data have been developed. Here we propose KBPRNA, a general computational framework for extracting specific kinase activities from bulk RNA-sequencing data in cancer samples. KBPRNA also achieves better performance in predicting kinase activities from bulk RNA-sequence data under cancer conditions benchmarking against other models.ResultsIn this study, we used LINCS-L1000 dataset which was used to be reported as efficient gene signatures in defining bulk RNA-seq data as input dataset of KBPRNA. Also, we utilized eXtreme Gradient Boosting (XGboost) as the main algorithm to extract valuable information to predict kinase activities. This model outperforms other methods such as linear regression and random forest in predicting kinase activities from bulk RNA-seq data. KBPRNA integrated tissue samples coming from breast invasive carcinoma, hepatocellular carcinoma, lung squamous cell carcinoma, Glioblastoma multiforme and Uterine Corpus Endometrial Carcinoma. It was found that KBPRNA achieved good performance with an average R score above threshold of 0.5 in kinase activity prediction.ConclusionsModel training and testing process showed that KBPRNA outperformed other machine learning methods in predicting kinase activities coming from various cancer types’ tissue samples. This model could be utilized to approximate basic kinase activities and link it with specific biological functions, which in further promoted the progress of cancer identification and prognosis.
Structural basis of sRNA RsmZ regulation ofPseudomonas aeruginosavirulence
Authors: Jia Xinyu , Pan Zhiling , Yuan Yang , Luo Bingnan , Luo Yongbo , Mukherjee Sunandan , Jia Guowen , Liu Liu , Ling Xiaobin , Yang Xiting , Miao Zhichao , Wei Xiawei , Bujnicki Janusz M. , Zhao Kelei , Su Zhaoming 
Published: November 2022

AbstractPseudomonas aeruginosais an opportunistic human pathogen that poses threats to hospitalized immunocompromised patients1. A non-coding small RNA (sRNA) from the repressor of secondary metabolites (Rsm) system, RsmZ, sequesters the global repressor protein RsmA to regulate downstream gene expressions that reprogram virulence repertoires associated with acute and chronicP. aeruginosainfections2,3. Molecular insights into the full-length RsmZ architecture remain elusive, leading to the lack of understanding of RsmZ binding to RsmA and subsequent modulations of gene expressions. Here we use cryo-electron microscopy (cryo-EM) to resolve structures of the full-length RsmZ in complexes with RsmA, in which five stem-loops (SLs) and one single-stranded junction carrying the GGA binding sites in RsmZ form three pairs of clamps, each binding to a RsmA homodimer. Disruptions of the base-pairings in all stems of RsmZ significantly reduced the binding affinity to RsmA by 17-fold, which resulted in enhanced RsmA downregulation of gene expressions and phenotypes associated to both acute and chronic virulence ofP. aeruginosa. Double mutations that rescued these stems of RsmZ restored the binding to RsmA by more than 5-fold, and recovered the corresponding phenotypes. Our results reveal the molecular mechanism of RsmZ regulation ofP. aeruginosavirulence and suggest RsmZ as a potential target for the development of new antimicrobial agents.
USP7/Maged1-Mediated H2A Monoubiquitination in the Paraventricular Thalamus: An Epigenetic Mechanism Involved in Cocaine Use Disorder
Authors: Cheron Julian , Beccari Leonardo , Hagué Perrine , Icick Romain , Despontin Chloé , Carusone Teresa , Defrance Mathieu , Bhogaraju Sagar , Martin-Garcia Elena , Capellan Roberto , Maldonado Rafael , Vorspan Florence , Bonnefont Jérôme , de Kerchove d’Exaerde Alban 
Published: November 2022

AbstractThe individual risk of developing drug addiction is highly determined by the epigenetic landscape1,2. Chromatin remodeling regulates drug-induced transcriptional and behavioral effects and the consequent development of addictive behaviors2,3. Several chromatin modifications in the ventral tegmental area and nucleus accumbens, including histone H3 methylation, H3 and H4 acetylation, have been implicated in drug addiction. Still, the contribution of other histones and their post-translational modifications (PTMs), such as monoubiquitination is unclear4–8. In the course of investigating the underlying mechanisms associated with melanoma-associated antigen D1 (Maged1)9, a scaffold protein involved in drug addiction9, we found that H2A monoubiquitination in the paraventricular thalamus (PVT) plays a major role in cocaine-adaptive behaviors and cocaine-evoked transcriptional repression. Mice undergoing chronic cocaine administration showed a significant increased monoubiquitination of H2A. Furthermore, we showed that this histone PTM is controlled, in the PVT, by Maged1, along with one of its partner, the deubiquitinase USP710. Accordingly, Maged1 specific inactivation in thalamic vGluT2 neurons, or USP7 inhibition, blocked cocaine-evoked H2A monoubiquitination and abolished cocaine locomotor sensitization. Finally, we identified genetic variations ofMAGED1andUSP7associated with modified transition to cocaine addiction and cocaine-induced aggressive behavior in human subjects. These findings identified a new epigenetic modification in a non-canonical reward pathway of the brain and a potent marker of epigenetic risk factor for drug addiction in human.
Direct conversion of human fibroblasts to pancreatic epithelial cells through transient progenitor states is controlled by temporal activation of defined factors
Authors: Fei Liangru , Zhang Kaiyang , Poddar Nikita , Hautaniemi Sampsa , Sahu Biswajyoti 
Published: November 2022

AbstractCell fate can be reprogrammed by ectopic expression of lineage-specific transcription factors (TF). For example, few specialized cell types like neurons, hepatocytes and cardiomyocytes have been generated from fibroblasts by defined factors (Wanget al, 2021). However, the exact cell state transitions and their control mechanisms during cell fate conversion are still poorly understood. Moreover, the defined TFs for generating vast majority of the human cell types are still elusive. Here, we report a novel protocol for reprogramming human fibroblasts to pancreatic exocrine cells with phenotypic and functional characteristics of ductal epithelial cells using a minimal set of six TFs. We mapped the molecular determinants of lineage dynamics at single-cell resolution using a novel factor-indexing method based on single-nuclei multiome sequencing (FI-snMultiome-seq) that enables dissecting the role of each individual TF and pool of TFs in cell fate conversion. We show that transdifferentiation – although being considered a direct cell fate conversion method – occurs through transient progenitor states orchestrated by stepwise activation of distinct TFs. Specifically, transition from mesenchymal fibroblast identity to epithelial pancreatic exocrine fate involves two deterministic steps: first, an endodermal progenitor state defined by activation of HHEX concurrently with FOXA2 and SOX17, and second, temporal GATA4 activation essential for maintenance of pancreatic cell fate program. Collectively, our data provide a high-resolution temporal map of the epigenome and transcriptome remodeling events that facilitate cell fate conversion, suggesting that direct transdifferentiation process occurs through transient dedifferentiation to progenitor cell states controlled by defined TFs.
MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport
Authors: Gossi Federico , Pati Pushpak , Martinelli Adriano , Rapsomaniki Maria Anna 
Published: November 2022

AbstractRecent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. However, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality of the data and the multiple sources of acquisition noise. In this work, we propose MatchCLOT, a novel method for single-cell data integration based on ideas borrowed from contrastive learning, optimal transport, and transductive learning. In particular, we use contrastive learning to learn a common representation between two modalities and apply entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance in the modality matching task from the NeurIPS 2021 multimodal single-cell data integration challenge, improving the previous best competition score by 28.9%. Our code can be accessed athttps://github.com/AI4SCR/MatchCLOT.
A combination of Metformin and Epigallocatechin Gallate Potentiates Glioma Chemotherapyin vivo
Authors: Kuduvalli Shreyas S , Precilla S Daisy , Vaithy Anandraj , Purushothaman Mugilarasi , Muralidharan Arumugam Ramachandran , Kumar B Agiesh , Mezger Markus , Antony Justin S , Subramani Madhu , Dubashi Biswajit , Biswas Indrani , Guruprasad K P , Anitha T.S 
Published: November 2022

AbstractGlioma is the most devastating high-grade tumor of the central nervous system, with dismal prognosis. Existing treatment modality does not provide substantial benefit to patients and demands novel strategies. One of the first-line treatments for glioma, temozolomide, provides marginal benefit to glioma patients. Repurposing of existing non-cancer drugs to treat oncology patients is gaining momentum in recent years. In this study, we investigated the therapeutic benefits of combining three repurposed drugs, namely, metformin (anti-diabetic) and epigallocatechin gallate (green tea-derived antioxidant) together with temozolomide in a glioma-induced xenograft rat model. Our triple-drug combination therapy significantly inhibited tumor growthin vivoand increased the survival rate (50%) of rats when compared with individual or dual treatments. Molecular and cellular analyses revealed that our triple-drug cocktail treatment inhibited glioma tumor growth in rat model through ROS-mediated inactivation of PI3K/AKT/mTOR pathway, arrest of the cell cycle at G1 phase and induction of molecular mechanisms of caspases-dependent apoptosis. In addition, the docking analysis and quantum mechanics studies performed here hypothesize that the effect of triple-drug combination could have been attributed by their difference in molecular interactions, that maybe due to varying electrostatic potential. Thus, repurposing metformin and epigallocatechin gallate and concurrent administration with temozolomide would serve as a prospective therapy in glioma patients.
Aneuploid embryos as a proposal for improving Artificial Intelligence performance
Authors: Güell Penas Enric , Esquerrà Parés Marina , Vives Perelló Andreu , Mladenova Koleva Mikaela 
Published: November 2022

AbstractRESEARCH QUESTIONCould we improve the performance of Machine Learning algorithms by using aneuploid embryos instead of non-implanted embryos as the contrary reference to Live-Birth embryos?DESIGNA single-center retrospective analysis of 343 embryos through 3 ML algorithms, based on manually annotated morphokinetics from Day 1 to Day 3. Two datasets were built including the same Live-Birth embryos (117). Dataset A included 123 non-implanted embryos, while Dataset B included 103 aneuploid embryos. V-Fold Cross-Validation was performed for each dataset and algorithm and the Area Under the Curve (AUC) was registered.RESULTSAUC for Dataset A did not reach 0.6 for any of the algorithms; while AUC values for “Dataset B” surpassed 0.7. According to this, different morphokinetic patterns were detected by Machine Learning algorithms.CONCLUSIONSAlgorithms’ minor performance with non-implanted embryos may be due to an increased Label Noise effect, suggesting that including aneuploid embryos could be more appropriate when building predictive algorithms for embryo viability. Machine Learning algorithms results were improved when aneuploid embryos were taken into consideration.
Screening of small molecule chemical library identifies leads that reduce cholesterol accumulation in human NPC1 fibroblasts
Authors: Pipalia Nina H. , Huang Amy Y. , Ralph Harold , Maxfield Frederick R. 
Published: November 2022

AbstractAlthough considerable progress has been made in finding a therapy for Niemann-Pick type C1 (NPC1) disease, there still is no effective approved treatment. Previously, we reported results from a small molecule screen that was conducted on Chinese Hamster Ovary (CHO) cells (CT60) that lacked NPC1 expression. The hit compounds that were effective in reducing cholesterol accumulation in CT60 cells were not effective on human cells bearing disease-causing mutations in NPC1. Hence, we conducted a new small molecule screen on patient-derived NPC1 skin fibroblasts using a combinatorial library of ~48,000 compounds. We have identified several compounds that corrected the phenotype in human heterozygous mutant NPC1 skin fibroblasts (GM03123) bearing the inactivating I1061T mutation in one allele and another set of inactivating mutations in the other. Among the hit compounds were several histone deacetylase inhibitors, which we have explored further and reported previously. With the determination of the structure of NPC1 protein and the development of computational docking algorithms, it seems worthwhile to show the structures of 128 compounds that reproducibly reduced cholesterol storage in mutant NPC1 fibroblasts but with no known mechanism of action. These compounds were not toxic and specifically effective on mutant cells but not on NPC1−/−cells, indicating on-target effect directly or indirectly.
The effect of background noise and its removal on the analysis of single-cell expression data
Authors: Janssen Philipp , Kliesmete Zane , Vieth Beate , Adiconis Xian , Simmons Sean , Marshall Jamie , McCabe Cristin , Heyn Holger , Levin Joshua Z. , Enard Wolfgang , Hellmann Ines 
Published: November 2022

AbstractBACKGROUNDIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Here, we characterize this background noise exemplified by three single-cell RNA-seq (scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise.RESULTSWe find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination and hence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure.CONCLUSIONOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.
Female-Specific Pituitary Hypersensitivity to Gonadotropin-Releasing Hormone in a Mouse Model of Chronic Temporal Lobe Epilepsy
Authors: Cutia Cathryn A. , Leverton Leanna K. , Weis Karen E. , Raetzman Lori T. , Christian-Hinman Catherine A. 
Published: November 2022

AbstractGonadotropin hormone release from the anterior pituitary is critical to regulating reproductive endocrine function. Clinical evidence has documented that people with epilepsy display altered levels of gonadotropin hormones, both acutely following seizures and chronically. Despite this relationship, pituitary function remains a largely understudied avenue in preclinical epilepsy research. Recently, we showed that females in the intrahippocampal kainic acid (IHKA) mouse model of temporal lobe epilepsy were found to display changes in pituitary expression of gonadotropin hormone and GnRH receptor genes. Circulating gonadotropin hormone levels, however, have yet to be measured in an animal model of epilepsy. Here, we evaluated the circulating levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), GnRH receptor (Gnrhr) gene expression, and sensitivity to exogenous GnRH in IHKA males and females. Although no changes in overall dynamics of pulsatile patterns of LH release were found in IHKA mice of either sex, estrus vs. diestrus changes in basal and mean LH levels were larger in IHKA females with prolonged, disrupted estrous cycles. In addition, IHKA females displayed increased pituitary sensitivity to GnRH and higherGnrhrexpression. The hypersensitivity to GnRH was observed on diestrus, but not estrus. Chronic seizure severity was not found to be correlated with LH parameters, and FSH levels were unchanged in IHKA mice. These results indicate that although there are changes in pituitary gene expression and sensitivity to GnRH in IHKA females, there may also be compensatory mechanisms that aid in maintaining gonadotropin release in the state of chronic epilepsy in this model.
The Cul5 E3 Ligase Complex Is a Key Negative Feedback Regulator of TCR/IL2 Signaling and Anti-Tumor Activity in CD8+T Cells
Authors: Liao Xiaofeng , Li Wenxue , Li Ao , Rajendran Barani Kumar , Ren Jingjing , Zhou Hongyue , Calderwood David , Turk Benjamin , Tang Wenwen , Liu Yansheng , Wu Dianqing 
Published: November 2022

AbstractCD8+T cells play an important role in tumor immune surveillance and control. Better understanding of the regulation of their anti-tumor actions and improving their cytotoxic function and persistence will help advancing cancer immunotherapies. Here, we report the development of a step-wise CRISPR knockout (KO) screening strategy under the selection of TGF-β, a clinically relevant immunosuppressive pressure. The screen identifies Cul5 as a negative-feedback regulator of the core signaling pathways, differentiation, and persistence of CD8+T cell. Cul5 KO in mouse CD8+T cells significantly improves their tumor control abilityin vitroandin vivowith significant proteomic alterations that generally enhance TCR and cytokine signaling, effector function, stemness, and survival of CD8+T cell. Mechanistically, Cul5, whose protein content and active, neddylated form increase upon TCR-stimulation, interacts with SOCS-box-containing Pcmtd2 and negatively regulates TCR and IL2/STAT5 signaling by decreasing TCR and IL2 signaling molecules. Moreover, Cul5 KO in human CD8+T cells phenocopies that in mouse CD8+T cells. Furthermore, KO of CTLA4 that is markedly upregulated by Cul5 KO in mouse and human CD8+cells further enhances anti-tumor effect of Cul5 KO, and a neddylation inhibitor enhances CD8 effector activities largely dependently of Cul5. These results together not only reveal a previously unknown negative-feedback regulatory mechanism for CD8+T cells, but also have strong translational implications in cancer immunotherapy.