bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nomogram-based survival predictions of patients with soft tissue cancer including heart in the United States
Authors: Ahmmad Roungu 
Published: January 2023

AbstractIntroductionThis study aimed to assess the effects of cancer treatment on sarcoma types and to predict overall survival probabilities using nomograms.MethodsThis study uses the SEER-18 database, Version 2020, sponsored by the National Cancer Institute (NCI). The study cohort included participants diagnosed between 2000 and 2018 with soft tissue cancers including heart. A multivariable Cox proportional hazards model was applied to predict mortality rate, and nomograms were used to predict overall survival probability.ResultsThe median survival time for 24,849 study participants was 48 months (IQR: 19-102) with Spindle Cell Sarcoma (ScS) having a shorter median survival time compared with Liposarcoma (LiS). A significant number of STS patients had surgery, where surgery on ScS improved survival by 75% (HR: 0.25, 95%CI: 0.19-0.32, p<0.001) and those who received radiation had a 26% improvement (HR: 0.74, 95%CI: 0.61-0.89, p=0.001). Furthermore, chemotherapy on GcS resulted in a 40% reduction in mortality for patients compared to those who did not receive chemotherapy (HR 0.60, 95%CI:0.45-0.80, p<0.001). Based on nomogram, after two, five, and ten years, a patient who received surgery on their primary sites would have a survival rate of approximately 90 percent. In contrast, a patient who did not receive surgery on their primary sites would only live for 20 percent or less. Patients with MyS have a 90% chance of surviving for 5 and 10 years after surgery, but only 22% and 10% without surgery.ConclusionsBased on the results of this study, surgical and radiation intervention on sarcomas was associated with improved survival in patients with STS, while chemotherapy and primary systemic therapy had contradictory effects.
A Physiologically-Based Pharmacokinetic Model for Tuberculosis Drug Disposition at Extrapulmonary Sites
Authors: Ramachandran Aparna , Gadgil Chetan J. 
Published: January 2023

AbstractTuberculosis (TB) is a leading cause of mortality due to an infectious agent. TB primarily targets the lungs but in about 16% cases can affect other organs as well, giving rise to extrapulmonary TB (EPTB). However, an optimal regimen for EPTB treatment is not defined. While the recommended treatment for most forms of EPTB is the same as pulmonary TB, pharmacokinetics of EPTB therapy are not as well-studied. To address this gap, we formulate a whole-body physiologically-based pharmacokinetic (PBPK) model for EPTB that for the first time includes the ability to simulate drug concentrations in the pleura and lymph node. Using this model, we estimate the time-dependent concentrations, at potential EPTB infection sites, of the 4 first-line anti-TB drugs: Rifampicin, Ethambutol, Isoniazid and Pyrazinamide. We utilise reported plasma concentration kinetics data to estimate model parameters for each drug, and validate our model using reported concentration data not used for model formulation or parameter estimation. Model predictions match the validation data and reported PK parameters (Cmax, tmax) for the drugs. The model also predicts Ethambutol, Isoniazid and Pyrazinamide concentrations in the pleura that match reported experimental values from an independent study. For each drug, the model is used to simulate the time-dependent drug concentrations at various EPTB sites. Predicted drug concentrations are compared to their critical concentration. Simulations suggest that while Rifampicin and Isoniazid concentrations are greater than critical concentration values at most EPTB sites, the concentrations of Ethambutol and Pyrazinamide are lower than their critical concentrations at most EPTB sites.
How well do rudimentary plasticity rules predict adult visual object learning?
Authors: Lee Michael J. , DiCarlo James J. 
Published: January 2023

AbstractA core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer. Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear – in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings. Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n=2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n=371,000 trials), which we then used to develop (andpublicly release) empirical benchmarks for comparing learning models to humans. We evaluated each learning model on these benchmarks, and found that learning models that using specific, high-level contemporary representations are surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.Author SummaryA basic conceptual hypothesis for how an adult brain learns to visually identify a new object is: 1) it re-represents images as points in a fixed, multidimensional space, then 2) it learns linear decision boundaries that separate images of a new object from others, using a single layer of plasticity. This hypothesis is considered biologically plausible, but gauging its power to explain human learning behavior has not been straightforward. In part, this is because it is difficult to model how brains re-represent images during object learning. However, ongoing efforts in neuroscience have led to the identification of specific, image-computable models that are at least partially accurate descriptions of the neural representations involved in primate vision. Here, we asked whether any of those representations, when combined them with simple plasticity rules, could make accurate predictions over a large body of human object learning behavioral measurements. We found that specific models could indeed explain a majority of our behavioral measurements, suggesting the rudimentary, biologically-plausible mechanisms considered here may be sufficient to explain a core aspect of human object learning.
Investigation of the Impact of Clonal Hematopoiesis on Severity and Pathophysiology of COVID-19 in Rhesus Macaques
Authors: Shin Tae-Hoon , Zhou Yifan , Lee Byung-Chul , Hong So Gun , Andrew Shayne F. , Flynn Barbara J. , Gagne Matthew , Todd John-Paul M. , Moore Ian N. , Cook Anthony , Lewis Mark G. , Foulds Kathryn E. , Seder Robert A. , Douek Daniel C. , Roederer Mario , Dunbar Cynthia E. 
Published: January 2023

ABSTRACTClinical manifestations of COVID-19 vary widely, ranging from asymptomatic to severe respiratory failure with profound inflammation. Although risk factors for severe illness have been identified, definitive determinants remain elusive. Clonal hematopoiesis (CH), the expansion of hematopoietic stem and progenitor cells bearing acquired somatic mutations, is associated with advanced age and hyperinflammation. Given the similar age range and hyperinflammatory phenotype between frequent CH and severe COVID-19, CH could impact the risk of severe COVID-19. Human cohort studies have attempted to prove this relationship, but conclusions are conflicting. Rhesus macaques (RMs) are being utilized to test vaccines and therapeutics for COVID-19. However, RMs, even other species, have not yet been reported to develop late inflammatory COVID-19 disease. Here, RMs with either spontaneous DNMT3A or engineered TET2 CH along with similarly transplanted and conditioned controls were infected with SARS-CoV-2 and monitored until 12 days post-inoculation (dpi). Although no significant differences in clinical symptoms and blood counts were noted, an aged animal with naturalDNMT3ACH died on 10 dpi. CH macaques showed evidence of sustained local inflammatory responses compared to controls. Interestingly, viral loads in respiratory tracts were higher at every timepoint in the CH group. Lung sections from euthanasia showed evidence of mild inflammation in all animals, while viral antigen was more frequently detected in the lung tissues of CH macaques even at the time of autopsy. Despite the lack of striking inflammation and serious illness, our findings suggest potential pathophysiological differences in RMs with or without CH upon SARS-CoV-2 infection.HighlightsNo evidence of association between CH and COVID-19 clinical severity in macaques.The presence of CH is associated with prolonged local inflammatory responses in COVID-19.SARS-CoV-2 persists longer in respiratory tracts of macaques with CH following infection.
SARS-CoV-2 protein NSP2 enhances microRNA-mediated translational repression
Authors: Naeli Parisa , Zhang Xu , Harris Snell Patric , Chatterjee Susanta , Kamran Muhammad , Ladak Reese Jalal , Orr Nick , Duchaine Thomas , Sonenberg Nahum , Jafarnejad Seyed Mehdi 
Published: January 2023

AbstractmicroRNAs (miRNAs) inhibit mRNA translation initiation by recruiting the GIGYF2/4EHP translation repressor complex to the mRNA 5’ cap structure. Viruses utilise miRNAs to impair the host antiviral immune system and facilitate viral infection by expressing their own miRNAs or co-opting cellular miRNAs. We recently reported that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encoded non-structural protein 2 (NSP2) interacts with GIGYF2. This interaction is critical for blocking translation of theIfn1-bmRNA that encodes the cytokine Interferon-ß, and thereby impairs the host antiviral immune response. However, it is not known whether NSP2 also affects miRNA-mediated silencing. Here, we demonstrate the pervasive augmentation of the miRNA-mediated translational repression of cellular mRNAs by NSP2. We show that NSP2 interacts with Argonaute 2, the core component of the miRNA-Induced Silencing Complex (miRISC) and enhances the translational repression mediated by natural miRNA binding sites in the 3’ UTR of cellular mRNAs. Our data reveal an additional layer of the complex mechanism by which SARS-CoV-2 and likely other coronaviruses manipulate the host gene expression program through co-opting the host miRNA-mediated silencing machinery.
The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain
Authors: Imani Elaheh , Hashemi Alireza , Radkani Setayesh , Egger Seth W. , Goudarzi Morteza Moazami 
Published: January 2023

AbstractAcross the cortical hierarchy, single neurons are characterized by differences in the extent to which they can sustain their firing rate over time (i.e., their “intrinsic timescale”). Previous studies have demonstrated that neurons in a given brain region mostly exhibit either short or long intrinsic timescales. In this study, we sought to identify populations of neurons that accumulate information over different timescales in the mouse brain and to characterize their functions in the context of a visual discrimination task. Thus, we separately examined the neural population dynamics of neurons with long or short intrinsic timescales across different brain regions. More specifically, we looked at the decoding performance of these neural populations aligned to different task variables (stimulus onset, movement). Taken together, our population-level findings support the hypothesis that long intrinsic timescale neurons encode abstract variables related to decision formation.Furthermore, we investigated whether there was a relationship between how well a single neuron represents the animal’s choice or stimuli and their intrinsic timescale. We did not observe any significant relationship between the decoding of these task variables and a single neuron’s intrinsic timescale. In summary, our findings support the idea that the long intrinsic timescale population of neurons, which appear at different levels of the cortical hierarchy, are primarily more involved in representing the decision variable.
Circuit to target approach defines an autocrine myofibroblast loop that drives cardiac fibrosis
Authors: Miyara Shoval , Adler Miri , Bassat Elad , Divinsky Yalin , Umansky Kfir B. , Elkahal Jacob , Genzelinakh Alexander , Kain David , Lendengolts Daria , Shalit Tali , Gershovits Michael , Shakked Avraham , Zhang Lingling , Wang Jingkui , Kimchi Danielle M. , Baehr Andrea , Sarig Rachel , Kupatt Christian , Tanaka Elly M. , Medzhitov Ruslan , Mayo Avi , Alon Uri , Tzahor Eldad 
Published: January 2023

AbstractFibrosis is a broad pathology of excessive scarring with substantial medical implications. The fibrotic scar is produced by myofibroblasts that interact with macrophages. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. Recently, we described a mathematical model for a macrophage-myofibroblast cell circuit, predicting two types of fibrosis - hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. To test these concepts and intervention strategies in a medically relevant system, we use a widely studiedin-vivoinjury model for fibrosis, myocardial infarction (MI). We show that cold fibrosis is the final outcome of MI in both mice and pigs and demonstrate that fibrosis can shift toward healing in regenerative settings. MI begind with an increase of myofibroblasts and macrophages, followed by macrophage decline leading to persistent cold fibrosis (only myofibroblasts). During this process, fibroblasts, unlike macrophages, acquire distinct fate changes. Using mathematical modeling we predict that targeting of the autocrine signal for myofibroblast division could block cold fibrosis. We identify TIMP1 as an autocrine cardiac myofibroblast growth factorin-vitro. Treatment of adult mice after MI with anti-TIMP1 antibodies reduces fibrosisin-vivo. This study shows the utility of the concepts of hot and cold fibrosis and the feasibility of our circuit-to-target approach to reduce fibrosis after acute cardiac injury by inhibiting the myofibroblast autocrine loop.
Bridging the Gap: Exploring Neural Mechanisms of Facial Repetition Suppression through EEG and DCNNs
Authors: Lu Zitong , Ku Yixuan 
Published: January 2023

AbstractFacial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Recent advancements have seen deep convolutional neural networks (DCNNs) achieve human-level performance in face recognition. In our present study, we sought to compare brain activation patterns derived from human electroencephalogram (EEG) data with those generated by DCNNs. Employing reverse engineering techniques, we aimed to provide a novel perspective on the neural mechanisms underlying facial repetition suppression. Our approach involved employing brain decoding methods to investigate how representations of faces change with their familiarity in the human brain. Subsequently, we constructed two models for repetition suppression within DCNNs: the Fatigue model, which posits that stronger activation leads to greater suppression, and the Sharpening model, which suggests that weaker activation results in more pronounced suppression. To elucidate the neural mechanisms at play, we conducted cross-modal representational similarity analysis (RSA) comparisons between human EEG signals and DCNN activations. Our results revealed a striking similarity between human brain representations and those of the Fatigue DCNN, favoring the Fatigue model over the Sharpening hypothesis in explaining the facial repetition suppression effect. These representation analyses, bridging the human brain and DCNNs, offer a promising tool for simulating brain activity and making inferences regarding the neural mechanisms underpinning complex human behaviors.
The Labbing Project: A database management application for neuroimaging research
Authors: Baratz Zvi , Assaf Yaniv 
Published: January 2023

AbstractThe goal of this article is to present “The Labbing Project”; a novel neuroimaging data aggregation and preprocessing web application built with Django and VueJS. Neuroimaging data can be complex and time-consuming to work with, especially for researchers with limited programming experience. This web application aims to streamline the process of aggregating and preprocessing neuroimaging data by providing an intuitive, user-friendly interface that allows researchers to upload, organize, and preprocess their data with minimal programming requirements. The application utilizes Django, a popular Python web framework, to create a robust and scalable platform that can handle large volumes of data and accommodate the needs of a diverse user base. This robust infrastructure is complemented by a user-friendly VueJS frontend application, supporting commonplace data querying and extraction tasks. By automating common data processing tasks, this web application aims to save researchers time and resources, enabling them to focus on their research rather than data management.
LptM promotes oxidative maturation of the lipopolysaccharide translocon by substrate binding mimicry
Authors: Yang Yiying , Chen Haoxiang , Corey Robin A. , Morales Violette , Quentin Yves , Froment Carine , Caumont-Sarcos Anne , Albenne Cécile , Burlet-Schiltz Odile , Ranava David , Stansfeld Phillip J. , Marcoux Julien , Ieva Raffaele 
Published: January 2023

ABSTRACTInsertion of lipopolysaccharide (LPS) into the outer membrane (OM) of Gram-negative bacteria is mediated by a druggable OM translocon consisting of a β-barrel membrane protein, LptD, and a lipoprotein, LptE. The β-barrel assembly machinery (BAM) assembles LptD together with LptE to form a plug-and-barrel structure. In the enterobacteriumEscherichia coli, formation of two native disulfide bonds in LptD controls LPS translocon activation. Here we report the discovery of LptM (formerly YifL), a conserved lipoprotein that assembles together with LptD and LptE at the BAM complex. We demonstrate that LptM stabilizes a conformation of LptD that can efficiently acquire native disulfide bonds and be released as mature LPS translocon by the BAM complex. Inactivation of LptM causes the accumulation of non-natively oxidized LptD, making disulfide bond isomerization by DsbC become essential for viability. Our structural prediction and biochemical analyses indicate that LptM binds to sites in both LptD and LptE that are proposed to coordinate LPS insertion into the OM. These results suggest that LptM facilitates oxidative maturation of LptD by mimicking LPS binding, thereby activating the LPS translocon.
Multiple independent components contribute to event-related potential correlates of conscious vision
Authors: Colombari Elisabetta , Railo Henry 
Published: January 2023

AbstractResearch has revealed two major event-related potential (ERP) markers of visual awareness: the earlier Visual Awareness Negativity (VAN, around 150–250 ms after stimulus onset), and the following Late Positivity (LP, around 300–500 ms after stimulus onset). Understanding the neural sources that give rise to VAN and LP is important in order to understand what kind of neural processes enable conscious visual perception. Although the ERPs afford high temporal resolution, their spatial resolution is limited because multiple separate sources sum up at the scalp level. In the present study, we sought to characterize the locations and time-courses of independent neural sources underlying the ERP correlates of visual awareness by means of Independent Component Analysis (ICA). ICA allows identifying and localizing the temporal dynamics of different neural sources that contribute to the ERP correlates of conscious perception. The present results show that while LP reflects a combination of multiple sources distributed among frontal, parietal and occipito-temporal cortex, the sources of VAN are localized to posterior areas including occipital and temporal cortex. In addition, our analysis reveals that activity in very early sources (roughly -100–100 ms after stimulus onset) in temporal and fronto-parietal cortices correlates with conscious vision.
Optimizing FreeSurfer’s Surface Reconstruction Parameters for Anatomical Feature Estimation
Authors: Baratz Zvi , Assaf Yaniv 
Published: January 2023

AbstractMagnetic resonance imaging (MRI) is a powerful tool for non-invasive imaging of the human body. However, the quality and reliability of MRI data can be influenced by various factors, such as hardware and software configurations, image acquisition protocols, and preprocessing techniques. In recent years, the introduction of large-scale neuroimaging datasets has taken an increasingly prominent role in neuroscientific research. The advent of publicly available and standardized repositories has enabled researchers to combine data from multiple sources to explore a wide range of scientific inquiries. This increase in scale allows the study of phenomena with smaller effect sizes over a more diverse sample and with greater statistical power.Other than the variability inherent to the acquisition of the data across sites, preprocessing and feature generation steps implemented in different labs introduce an additional layer of variability which may influence consecutive statistical procedures. In this study, we show that differences in the configuration of surface reconstruction from anatomical MRI using FreeSurfer results in considerable changes to the estimated anatomical features. In addition, we demonstrate the effect these differences have on within-subject similarity and the performance of basic prediction tasks based on the derived anatomical features.Our results show that although FreeSurfer may be provided with either a T2w or a FLAIR scan for the same purpose of improving pial surface estimation (relative to based on the mandatory T1w scan alone), the two configurations have a distinctly different effect. In addition, our findings indicate that the similarity of within-subject scans and performance of a range of models for the prediction of sex and age are significantly effected, they are not significantly improved by either of the enhanced configurations. These results demonstrate the large extent to which elementary and sparsely reported differences in preprocessing workflow configurations influence the derived brain features.The results of this study are meant to underline the importance of optimizing preprocessing procedures based on experimental results prior to their distribution and consecutive standardization and harmonization efforts across public datasets. In addition, preprocessing configurations should be carefully reported and included in any following analytical workflows, to account for any variation originating from such differences. Finally, other representations of the raw data should be explored and studied to provide a more robust framework for data aggregation and sharing.