bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting intelligence from fMRI data of the human brain in a few minutes of scan time
Authors: Lohmann Gabriele , Lacosse Eric , Ethofer Thomas , Kumar Vinod J. , Scheffler Klaus , Jost Jürgen 
Published: March 2021

AbstractIn recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10 minutes versus 1 hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that it achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight.
Dynamic changes of genomic and transcriptomic tumor diversity during melanoma progression
Authors: Moore Ariane L. , Dimitrakopoulos Christos , Flückiger-Mangual Stefanie , Frischknecht Lukas , Beisel Christian , Krek Wilhelm , Beerenwinkel Niko 
Published: March 2021

AbstractMetastatic melanoma is an aggressive disease that is notoriously difficult to treat. Here, we used a BRAFV600E-driven mouse model to study melanoma progression and metastasis in the absence of extrinsic mutagenic factors such as UV radiation. The tumors were grown for different time periods in seven different mice such that disease progression was tracked over time. We sequenced the whole exome and whole transcriptome of 16 melanoma samples from these mice. The timed samples revealed that many genes are increasingly upregulated during progression, such as genes involved in extracellular matrix organization, andVegfc, a factor that induces lymphatic vessel growth and metastases to lymph nodes, which was identified as an important signaling hub in our analysis. TheVegfcgene interaction network highlighted multiple genes from the MAPK pathway that are increasingly deregulated during tumor evolution. The genomic diversity of primary and metastatic tumors showed an increase over time. Additionally, our integrative multi-omics analysis adds further evidence thatRock1is a crucial player driving metastasis formation, and may be upregulated due to mutations in the PI3K/AKT pathway. In summary, our study contributes towards an improved understanding of melanoma which may eventually lead to improved treatment strategies.
Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles
Authors: Jackson Heather , Menikou Stephanie , Hamilton Shea , McArdle Andrew , Shimizu Chisato , Galassini Rachel , Huang Honglei , Kim Jihoon , Tremoulet Adriana , de Jonge Marien , Kuijpers Taco , Wright Victoria , Burns Jane , Casals-Pascual Climent , Herberg Jethro , Levin Mike , Kaforou Myrsini 
Published: March 2021

AbstractThe aetiology of Kawasaki Disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infection at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.
Antigen-specific stimulation and expansion of CAR-T cells using membrane vesicles as target cell surrogates
Authors: Ukrainskaya V.M. , Rubtsov Y.P. , Pershin D.S. , Podoplelova N.A. , Terekhov S.S. , Kalinin R.S. , Yaroshevich I.A. , Sokolova A.I. , Bagrov D.V. , Kulakovskaya E.A. , Shipunova V.O. , Deyev S.M. , Maksimov E.G. , Markov O.V. , Oshchepkova A.L. , Zenkova M.A. , Xie J. , Gabibov A.G. , Maschan M.A. , Stepanov A.V. , Lerner. R.A. 
Published: March 2021

AbstractDevelopment of CAR-T therapy led to immediate success in the treatment of B cell leukemia and lymphoma. It also raised an opportunity to design new protocols to target solid tumors. Manufacturing of therapy-competent functional CAR-T cells needs robust protocols forex vivo/in vitroexpansion of modified T-cells. This step is challenging, especially if non-viral low efficiency delivery protocols are used to generate CAR-T cells. Modern protocols for CAR-T cell expansion are based on incubation with high doses of recombinant cytokines to support proliferation, non-specific stimulation with surface-bound antibodies to induce TCR cross-linking, or co-cultivation with antigen-expressing feeder cell lines. These approaches are imperfect since non-specific stimulation results in rapid outgrowth of CAR-negative T cells, and removal of feeder cells from mixed cultures necessitates additional purification steps. In an effort to develop a specific and improved protocol for CAR-T cell expansion, we took advantage of cell-derived membrane vesicles, and the simple structural demands of the CAR-antigen interaction. Our approach was to make antigenic microcytospheres from common cell lines stably expressing surface-bound CAR antigens (antigenic vesicles, AVs), and then use them for stimulation and expansion of CAR-T cells. We developed a rapid, simple, efficient, and inexpensive protocol to generate, stabilize and purify AVs. As proof-of-concept we tested the efficacy of our AV constructs on several CAR-antigen pairs. The data presented in this article clearly demonstrate that our protocol produced AVs with the capacity to induce stronger stimulation, proliferation and functional activity of CAR-T cells than is possible with existing protocols. We predict that this new methodology will significantly improve the ability to obtain improved populations of functional CAR-T cells for therapy.Graphical abstract
Exploration-based learning of a stabilizing controller predicts locomotor adaptation
Authors: Seethapathi Nidhi , Clark Barrett , Srinivasan Manoj 
Published: March 2021

ABSTRACTHumans adapt their locomotion seamlessly in response to changes in the body or the environment. We do not understand how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controller’s performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons — capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.
Deep Learning Shows Cellular Senescence Is a Barrier to Cancer Development
Authors: Heckenbach Indra , Ezra Michael Ben , Mkrtchyan Garik V , Madsen Jakob Sture , Nielsen Malte Hasle , Oró Denise , Mortensen Laust , Verdin Eric , Westendorp Rudi , Scheibye-Knudsen Morten 
Published: March 2021

AbstractCellular senescence is a critical component of aging and many age-related diseases, but understanding its role in human health is challenging in part due to the lack of exclusive or universal markers. Using neural networks, we achieve high accuracy in predicting senescence state and type from the nuclear morphology of DAPI-stained human fibroblasts, murine astrocytes and fibroblasts derived from premature aging diseasesin vitro. After generalizing this approach, the predictor recognizes an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating corresponding medical records reveals that individuals with increased senescent cells have a significantly decreased rate of malignant neoplasms, lending support for the protective role of senescence in limiting cancer development. In sum, we introduce a novel predictor of cellular senescence and apply it to diagnostic medical images, indicating cancer occurs more frequently for those with a lower rate of senescence.
Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions - having an eye on the ‘cooperative eye hypothesis’
Authors: Caspar Kai R. , Biggemann Marco , Geissmann Thomas , Begall Sabine 
Published: March 2021

AbstractPigmentation patterns of the visible part of the eyeball, encompassing the iris and portions of the sclera, have been discussed to be linked to social cognition in primates. In the context of thecooperative eye hypothesis, the white sclera of humans has been viewed as a derived adaptive trait, enhancing communication via glance cueing. Here, we provide a comparative analysis of ocular pigmentation patterns in 15 species of hominoids (humans, great apes & gibbons) representing all extant ape genera, based on photographs and literature data. Additionally, we quantify hominoid scleral exposure on the genus level during different glancing situations. Our data reveals a continuum of eye pigmentation traits among the studied taxa. Gibbons display darker, more uniformly colored eyes than great apes and expose less sclera, particularly during averted glancing. Iridoscleral contrasts in orangutans and gorillas approach the human condition but differ between congeneric species. Contrary to recent discussions, we found chimpanzee eyes to exhibit a cryptic coloration scheme that resembles gibbons more than other great apes and that does not enhance glance cueing or gaze following. We critically evaluate the evidence for links between social cognition and eye pigmentation in primates, concluding that thecooperative eye hypothesiscannot convincingly explain the patterns observed. Although the human eye exhibits unique traits that are likely linked to social communication, high iridoscleral contrast is not one of them. Differences in scleral pigmentation between great apes and humans are gradual and might have arisen via genetic drift and sexual selection.
Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants
Authors: van Bueren Nienke E.R. , Reed Thomas L. , Nguyen Vu , Sheffield James G. , van der Ven Sanne H.G. , Osborne Michael A. , Kroesbergen Evelyn H. , Cohen Kadosh Roi 
Published: March 2021

AbstractAccumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.
Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions
Authors: Seo Sangmin , Choi Jonghwan , Park Sanghyun , Ahn Jaegyoon 
Published: March 2021

AbstractAccurate prediction of protein-ligand binding affinity is important in that it can lower the overall cost of drug discovery in structure-based drug design. For more accurate prediction, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient interactions energy terms to describe complex interactions between proteins and ligands. Recent deep-learning techniques show strong potential to solve this problem, but the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complexes continues. In this study, we proposed a deep-neural network for more accurate prediction of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings that contains embedded information about the local structures of a protein-ligand complex and an attention mechanism for highlighting important descriptors to binding affinity prediction. The proposed model showed better performance on most benchmark datasets than existing binding affinity prediction models. Moreover, we confirmed that an attention mechanism was able to capture binding sites in a protein-ligand complex and that it contributed to improvement in predictive performance. Our code is available athttps://github.com/Blue1993/BAPA.Author summaryThe initial step in drug discovery is to identify drug candidates for a target protein using a scoring function. Existing scoring functions, however, lack the ability to accurately predict the binding affinity of protein-ligand complexes. In this study, we proposed a deep learning-based approach to extract patterns from the local structures of protein-ligand complexes and to highlight the important local structures via an attention mechanism. The proposed model showed good performance for various benchmark datasets compared to existing models.
CONSULT: Accurate contamination removal using locality-sensitive hashing
Authors: Rachtman Eleonora , Bafna Vineet , Mirarab Siavash 
Published: March 2021

AbstractA fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skims-based mitochondrial assemblies.
Phosphorylation of Ribosomal Protein S6 differentially affects mRNA translation based on ORF length
Authors: Bohlen Jonathan , Teleman Aurelio A. 
Published: March 2021

ABSTRACTPhosphorylation of Ribosomal Protein S6 (RPS6) was the first post-translational modification of the ribosome to be identified and is a commonly-used readout for mTORC1 activity. Although the cellular and organismal functions of RPS6 phosphorylation are known, its molecular consequences on translation are less well understood. Here we use selective ribosome footprinting to analyze the location of ribosomes containing phosphorylated RPS6 on endogenous mRNAs in cells. We find that RPS6 becomes progressively dephosphorylated on ribosomes as they translate an mRNA. As a consequence, average RPS6 phosphorylation is higher on mRNAs with short coding sequences (CDSs) compared to mRNAs with long CDSs. In particular, ribosomes translating on the endoplasmic reticulum are more rapidly dephosphorylated than cytosolic ribosomes. Loss of RPS6 phosphorylation causes a correspondingly larger drop in translation efficiency of mRNAs with short CDSs than long CDSs. Interestingly, mRNAs with 5’ TOP motifs are translated well also in the absence of RPS6 phosphorylation despite short CDS lengths, suggesting they are translated via a different mode. In sum this provides a dynamic view of RPS6 phosphorylation on ribosomes as they translate mRNAs in different subcellular localizations and the functional consequence on translation.
A new drug candidates for glycogen storage disorders enhances glycogen catabolism: Lessons from Adult Polyglucosan Body Disease models
Authors: Vaknin Hilla , Mishra Kumudesh , D’Souza Jeevitha , Marisat Monzer , Sprecher Uri , Wald-Altman Shane , Dukhovny Anna , Raviv Yuval , Da’adoosh Benny , Engel Hamutal , Benhamron Sandrine , Nitzan Keren , Permyakova Anna , Rosenmann Hanna , Lossos Alexander , Tam Joseph , Minassian Berge A. , Kakhlon Or , Weil Miguel 
Published: March 2021

AbstractThis work employs Adult Polyglucosan Body Disease (APBD) models to explore the efficacy and mechanism of action of 144DG11, a new polyglucosan-reducing lead compound discovered by a high-throughput screen (HTS). APBD is an adult onset glycogen storage disorder (GSD) manifesting as a debilitating progressive axonopathic leukodystrophy. APBD is caused by glycogen branching enzyme (GBE) deficiency leading to poorly branched and insoluble glycogen inclusions, which precipitate as neuropathogenic polyglucosans (PG). 144DG11 led to prolonged survival and improved motor parameters in a GBE knockin (Gbeys/ys) APBD mouse model. Histopathologically, 144DG11 reduced PG and glycogen levels in brain, liver, heart, and peripheral nerve. Indirect calorimetry experiments revealed that 144DG11 increases carbohydrate burn at the expense of fat burn, suggesting metabolic mobilization of pathogenic PG. These results were also reflected at the cellular level by increased glycolytic, mitochondrial and total ATP production. Mechanistically, we show that the molecular target of 144DG11 is the lysosomal membrane protein LAMP1, whose interaction with the compound, similar to LAMP1 knockdown, enhanced autolysosomal degradation of glycogen and lysosomal acidification. Enhanced mitochondrial activity and lysosomal modifications were also the most pronounced effects of 144DG11 in APBD patient fibroblasts as discovered by image-based multiparametric phenotyping analysis and corroborated by proteomics. In summary, this work presents a broad mechanistic and target-based characterization of 144DG11 in in vivo and cell models of the prototypical GSD APBD. This investigation warrants development of 144DG11 into a safe and efficacious GSD therapy.One Sentence SummaryA new compound, demonstrated to ameliorate APBD in vivo and ex vivo by autophagic catabolism of glycogen, may potentially become a universal drug for glycogen storage disorders.
Nonsense Mediated RNA Decay Is a Unique Vulnerability of Cancer Cells with SF3B1 and U2AF1 Mutations
Authors: Cheruiyot Abigael , Li Shan , Srivatsan Sridhar Nonavinkere , Ahmed Tanzir , Chen Yuhao , Lemacon Delphine Sangotokun , Li Ying , Yang Zheng , Wadugu Brian A. , Warner Wayne , Pruett-Miller Shondra M. , Obeng Esther A. , Link Daniel C. , He Dalin , Xiao Fei , Wang Xiaowei , Bailis Julie M. , Walter Matthew J. , You Zhongsheng 
Published: March 2021

AbstractNonsense-mediated RNA decay (NMD) is well recognized as an RNA surveillance pathway that targets aberrant mRNAs with premature translation termination codons (PTCs) for degradation; however, its molecular mechanisms and roles in health and disease remain incompletely understood. In this study, we developed a novel reporter system that can accurately measure NMD activity in individual cells. By carrying out a genome-wide CRISPR/Cas9 knockout screen using this reporter system, we identified novel NMD-promoting factors, including multiple components of the SF3B complex and other U2 spliceosome factors. Interestingly, we also found that cells with mutations in the U2 spliceosome genesSF3B1andU2AF1—which are commonly found in myelodysplastic syndrome (MDS) and cancers—have overall attenuated NMD activity. Furthermore, we found that compared to wild type cells,SF3B1andU2AF1mutant cells are more sensitive to NMD inhibition, a phenotype that is accompanied by elevated DNA replication obstruction, DNA damage and chromosomal instability. Remarkably, the sensitivity of spliceosome mutant cells to NMD inhibition could be rescued by overexpression of RNase H1, which removes R-loops in the genome. Together, our findings shed new light on the functional interplay between NMD and RNA splicing and suggest a novel strategy for the treatment of MDS and cancers with spliceosome mutations.