bluuebunny/crossref_metadata_2025_split huggingface dataset:
Substantia nigra degradation results in widespread changes in medial zona incerta afferent and efferent connectomics
Authors: Kim Linda H. , Chomiak Taylor , Tran Michelle A. , Tam Stephanie , McPherson Claire , Eaton Shane E. A. , Ou Young , Kiss Zelma H. T. , Whelan Patrick J. 
Published: September 2021

AbstractParkinson’s disease (PD) is a complex disease affecting many facets of movement, especially gait abnormalities such as shuffling and freezing of gait. The nigrostriatal pathways of the basal ganglia are traditionally targeted by existing therapies; however, other pathways may be more relevant to gait, such as the pedunculopontine nucleus and the zona incerta (ZI). The A13 nucleus may be such a target as it has emerged as an area of interest in dopamine motor function. Yet, this area remains understudied compared to other dopamine nuclei, especially in animal models of PD. In 6-OHDA mice, we found a reduction in locomotion in the open field and gait dysfunction during treadmill tests. Medial ZI dopamine cells, containing the A13 nucleus, were preserved following 6-OHDA, in contrast to a marked reduction in substantia nigra pars compacta (SNc) neurons. There was extensive remodelling of the A13 afferent and efferent connectome following nigrostriatal lesions. Afferent input patterns displayed a marked reduction in cross-correlation across brain regions in 6-OHDA mice, while efferent projections showed an increase. In a human PD patient with advanced gait dysfunction we found that the A13 nucleus was preserved, suggesting that remodelling could also occur in humans. This work points to the A13 region as a potential therapeutic target in PD.Significance StatementRecently it was found that the medial zona incerta projects to the cuneiform nucleus suggesting a parallel dopaminergic projection onto motor regions. Here we investigated the connectome of the A13 region and examined the afferent and efferent projections in normal mice and mice with a unilateral Parkinsonian mouse model. We found that the connectome was reconfigured following nigrostriatal degeneration. This work provides a comprehensive insight into the plasticity in a dopaminergic-rich area of the zona incerta in PD injury models.
Framework estimation of stochastic gene activation using transcription average level
Authors: Chen Liang , Lin Genghong , Jiao Feng 
Published: September 2021

AbstractGene activation is usually a non-Markovian process that has been modeled as various frameworks that consist of multiple rate-limiting steps. Understanding the exact activation framework for a gene of interest is a central problem for single-cell studies. In this paper, we focus on the dynamical data of the average transcription level M (t), which is typically neglected when deciphering gene activation. Firstly, the smooth trend lines of M (t) data present rich, visually dynamic features. Secondly, tractable analysis of M (t) allows the establishment of bijections between M (t) dynamics and system parameter regions. Because of these two clear advantages, we can rule out frameworks that fail to capture M (t) features and we can further test potential competent frameworks by fitting M (t) data. We implemented this procedure to determine an exact activation framework for a large number of mouse fibroblast genes under tumor necrosis factor induction; the cross-talk between the signaling and basal pathways is crucial to trigger the first peak of M (t), while the following damped gentle M (t) oscillation is regulated by the multi-step basal pathway. Moreover, the fitted parameters for the mouse genes tested revealed two distinct regulation scenarios for transcription dynamics. Taken together, we were able to develop an efficient procedure for using traditional M (t) data to estimate the gene activation frameworks and system parameters. This procedure, together with sophisticated single-cell transcription data, may facilitate a more accurate understanding of stochastic gene activation.Author SummaryIt has been suggested that genes randomly transit between inactive and active states, with mRNA produced only when a gene is active. The gene activation process has been modeled as a framework of multiple rate-limiting steps listed sequentially, parallel, or in combination. The system step numbers and parameters can be predicted by computationally fitting sophisticated single-cell transcription data. However, current algorithms require a prior hypothetical framework of gene activation. We found that the prior estimation of the framework can be achieved using the traditional dynamical data of mRNA average level M (t) which present easily discriminated dynamical features. The theory regarding M (t) profiles allows us to confidently rule out other frameworks and to determine optimal frameworks by fitting M (t) data. We successfully applied this procedure to a large number of mouse fibroblast genes and confirmed that M (t) is capable of providing a reliable estimation of gene activation frameworks and system parameters.
Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data
Authors: Lotfollahi Mohammad , Dony Leander , Agarwala Harshita , Theis Fabian 
Published: September 2021

AbstractLearning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data usingβvariational autoencoder (β-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show thatβ-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.
A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission
Authors: Hansen Lasse , Zhang Yan-Ping , Wolf Detlef , Sechidis Konstantinos , Ladegaard Nicolai , Fusaroli Riccardo 
Published: September 2021

AbstractObjectiveAffective disorders are associated with atypical voice patterns; however, automated voice analyses suffer from small sample sizes and untested generalizability on external data. We investigated a generalizable approach to aid clinical evaluation of depression and remission from voice using transfer learning: we train machine learning models on easily accessible non-clinical datasets and test them on novel clinical data in a different language.MethodsA Mixture-of-Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora in German and US English. We examined the model’s predictive ability to classify the presence of depression on Danish speaking healthy controls (N = 42), patients with first-episode major depressive disorder (MDD) (N = 40), and the subset of the same patients who entered remission (N = 25) based on recorded clinical interviews. The model was evaluated on raw, de-noised, and speaker-diarized data.ResultsThe model showed separation between healthy controls and depressed patients at the first visit, obtaining an AUC of 0.71. Further, speech from patients in remission was indistinguishable from that of the control group. Model predictions were stable throughout the interview, suggesting that 20-30 seconds of speech might be enough to accurately screen a patient. Background noise (but not speaker diarization) heavily impacted predictions.ConclusionA generalizable speech emotion recognition model can effectively reveal changes in speaker depressive states before and after remission in patients with MDD. Data collection settings and data cleaning are crucial when considering automated voice analysis for clinical purposes.Significant outcomes- Using a speech emotion recognition model trained on other languages, we predicted the presence of MDD with an AUC of 0.71.- The speech emotion recognition model could accurately detect changes in voice after patients achieved remission from MDD.- Preprocessing steps, particularly background noise removal, greatly influenced classification performance.Limitations- No data from non-remitters, meaning that changes to voice for that group could not be assessed.- It is unclear how well the model would generalize beyond Germanic languages.Data availability statementDue to the nature of the data (autobiographical interviews in a clinical population), the recordings of the participants cannot be shared publicly. The aggregated model predictions and code used to run the analyses is available athttps://github.com/HLasse/SERDepressionDetection.
HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data
Authors: Xie Qing , Han Chenggong , Jin Victor , Lin Shili 
Published: September 2021

AbstractSingle cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.
Kronos scRT: a uniform framework for single-cell replication timing analysis
Authors: Gnan Stefano , Josephides Joseph M. , Wu Xia , Spagnuolo Manuela , Saulebekova Dalila , Bohec Mylène , Dumont Marie , Baudrin Laura G. , Fachinetti Daniele , Baulande Sylvain , Chen Chun-Long 
Published: September 2021

AbstractMammalian genomes are replicated in a cell-type specific order and in coordination with transcription and chromatin organization. Although the field of replication is also entering the single-cell era, current studies require cell sorting, individual cell processing and have yielded a limited number (<100) of cells. Here, we have developed Kronos scRT (https://github.com/CL-CHEN-Lab/Kronos_scRT), a software for single-cell Replication Timing (scRT) analysis. Kronos scRT does not require a specific platform nor cell sorting, allowing the investigation of large datasets obtained from asynchronous cells. Analysis of published available data and droplet-based scWGS data generated in the current study, allows exploitation of scRT data from thousands of cells for different mouse and human cell lines. Our results demonstrate that, although most cells replicate within a close timing range for a given genomic region, replication can also occur stochastically throughout S phase. Altogether, Kronos scRT allows investigating the RT program at a single-cell resolution for both homogeneous and heterogeneous cell populations in a fast and comprehensive manner.
Sex differences in the reward value of familiar mates in prairie voles
Authors: Vahaba Daniel M. , Halstead Emily R. , Donaldson Zoe R. , Ahern Todd H. , Beery Annaliese K. 
Published: September 2021

AbstractThe rewarding properties of social interactions facilitate relationship formation and maintenance. Prairie voles are one of the few laboratory species that form selective relationships, manifested as “partner preferences” for familiar partners versus strangers. While both sexes exhibit strong partner preferences, this similarity in outward behavior likely results from sex-specific neurobiological mechanisms. We recently used operant conditioning to demonstrate that females work harder for access to a familiar versus unfamiliar conspecific of either sex, while males worked equally hard for access to any female, indicating a key sex difference in social motivation. As tests were performed with one social target at a time, males might have experienced a ceiling effect, and familiar females might be more relatively rewarding in a choice scenario. Here we performed a social choice operant task in which voles could repeatedly lever-press to gain temporary access to either the chamber containing their mate or one containing a novel opposite-sex vole. Females worked hardest to access their mate, while males pressed at similar rates for either female. Individual male behavior was heterogeneous, congruent with multiple mating strategies in the wild. Voles exhibited preferences for favorable over unfavorable environments in a non-social operant task, indicating that lack of social preference does not reflect lack of discrimination between chambers. Oxytocin receptor genotype at the intronic SNP NT213739 replicated a prior association with stranger-directed aggression within the test. These findings suggest that convergent preference behavior in male and female voles results from sex-divergent pathways, particularly in the realm of social motivation.
Determination of hemicellulose, cellulose, holocellulose and lignin content using FTIR inCalycophyllum spruceanum(Benth.) K.Schum. andGuazuma crinitaLam.
Authors: Javier-Astete Rosario , Jimenez-Davalos Jorge , Zolla Gaston 
Published: September 2021

AbstractCapirona (Calycophyllum spruceanum(Benth.) K. Schum.) and Bolaina (Guazuma crinitaLam.) are fast-growing Amazonian trees with increasing demand in timber industry. Therefore, it is necessary to determine the content of cellulose, hemicellulose, holocellulose and lignin in juvenile tress to accelerate forest breeding programs. The aim of this study was to identify chemical differences between apical and basal stem of Capirona and Bolaina to develop models for estimating the chemical composition using Fourier transform infrared (FTIR) spectra. FTIR-ATR spectra were obtained from 150 samples for each specie that were 1.8 year-old. The results showed significant differences between the apical and basal stem for each species in terms of cellulose, hemicellulose, holocellulose and lignin content. This variability was useful to build partial least squares (PLS) models from the FTIR spectra and they were evaluated by root mean squared error of predictions (RMSEP) and ratio of performance to deviation (RPD). Lignin content was efficiently predicted in Capirona (RMSEP = 0.48, RPD > 2) and Bolaina (RMSEP = 0.81, RPD > 2). In Capirona, the predictive power of cellulose, hemicellulose and holocellulose models (0.68 < RMSEP < 2.06, 1.60 < RPD < 1.96) were high enough to predict wood chemical composition. In Bolaina, model for cellulose attained an excellent predictive power (RMSEP = 1.82, RPD = 6.14) while models for hemicellulose and holocellulose attained a good predictive power (RPD > 2.0). This study showed that FTIR-ATR together with PLS is a reliable method to determine the wood chemical composition in juvenile trees of Capirona and Bolaina.
Redox-dependent Structure and Dynamics of Macrophage Migration Inhibitory Factor Reveal Sites of Latent Allostery
Authors: Skeens Erin , Gadzuk-Shea Meagan , Shah Dilip , Bhandari Vineet , Schweppe Devin K. , Berlow Rebecca B. , Lisi George P. 
Published: September 2021

AbstractMacrophage migration inhibitory factor (MIF) is a multifunctional immunoregulatory protein that is a key player in the innate immune response. Given its overexpression at sites of inflammation in a wide range of diseases marked by increasingly oxidative cellular environment, a comprehensive structural understanding of how cellular redox conditions may impact the structure and function of MIF is necessary. We used solution NMR spectroscopy and mass spectrometry to investigate structural and dynamic signatures of MIF under varied solution redox conditions. Our results indicate that the MIF structure is modified and becomes increasingly dynamic in an oxidative environment, which may be a means to alter the MIF functional response in a redox-dependent manner. We identified latent allosteric sites within MIF that are redox-sensitive and mutational analysis reveals that loss of redox-responsive residues attenuates activation of the coreceptor CD74. Leveraging sites of redox-sensitivity therefore reveals an avenue to modulate MIF function in its “disease state” via structure-based drug design.
Targeting the translational machinery in gastrointestinal stromal tumors (GIST) – a new therapeutic vulnerability
Authors: Lee Donna M. , Sun Angela , Patil Sneha S. , Liu Lijun , Rao Aparna , Trent Parker , Ali Areej A. , Liu Catherine , Rausch Jessica L. , Presutti Laura , Kaczorowski Adam , Schneider Felix , Amankulor Nduka , Shuda Masahiro , Duensing Anette 
Published: September 2021

ABSTRACTAlthough KIT-mutant GISTs can be effectively treated with tyrosine kinase inhibitors (TKIs), many patients develop resistance to imatinib mesylate (IM) as well as the FDA-approved later-line agents sunitinib, regorafenib and ripretinib. Resistance mechanisms mainly involve secondary mutations in the KIT receptor tyrosine kinase gene indicating continued dependency on the KIT signaling pathway. The fact that the type of secondary mutation confers either sensitivity or resistance towards TKIs and the notion that secondary mutations exhibit intra- and intertumoral heterogeneity complicates the optimal choice of treatment in the imatinib-resistant setting. Therefore, new strategies that target KIT independently of its underlying mutations are urgently needed. Homoharringtonine (HHT) is a first-in-class inhibitor of protein biosynthesis and is FDA-approved for the treatment of chronic myeloid leukemia (CML) that is resistant to at least two TKIs. HHT has also shown activity in KIT-mutant mastocytosis models, which are intrinsically resistant to imatinib and most other TKIs. We hypothesized that HHT could be effective in GIST through downregulation of KIT expression and subsequent decrease of KIT activation and downstream signaling. Testing several GIST cell line models, HHT led to a significant reduction in nascent protein synthesis and was highly effective in the nanomolar range in IM-sensitive and IM-resistant GIST cell lines. HHT treatment resulted in a rapid and complete abolishment of KIT expression and activation, while KIT mRNA levels were minimally affected. The response to HHT involved induction of apoptosis as well as cell cycle arrest. The antitumor activity of HHT was confirmed in a GIST xenograft model. Taken together, inhibition of protein biosynthesis is a promising strategy to overcome TKI resistance in GIST.
Transcriptional control of hypoxic hyphal growth in the fungal pathogen Candida albicans
Authors: Henry Manon , Burgain Anais , Tebbji Faiza , Sellam Adnane 
Published: September 2021

AbstractBackgroundThe ability of Candida albicans, an important human fungal pathogen, to develop filamentous forms is a crucial determinant for host invasion and virulence. Filamentation is triggered by different host environmental cues. Hypoxia, the dominant conditions that C. albicans encounters inside the human host, promote filamentation, however, the contributing mechanisms remain poorly characterized.MethodsWe performed a quantitative analysis of gene deletion mutants from different collections of protein kinases and transcriptional regulators in C. albicans to identify specific modulators of the hypoxic filamentation. We used genome-wide transcriptional profiling (Microarrays) and promoter occupancy (ChIP-chip) to characterize regulons of two transcription factors that were associated with the hypoxic filamentation. Genetic interactions were also used to assess functional relationships among the newly identified modulators of hypoxic filamentation and the well-known C. albicans core morphogenetic regulators.ResultsOur genetic screen uncovered two transcription factors, Ahr1 and Tye7, that act as prominent regulators of C. albicans filamentation specifically under hypoxia. Both ahr1 and tye7 mutants exhibited a hyperfilamentous phenotype specifically under an oxygen-depleted environment suggesting that these transcription factors act as negative regulators of hypoxic filamentation. By combining microarray and ChIP-chip data, we have characterized the set of genes that are directly modulated by Ahr1 and Tye7. We found that both Ahr1 and Tye7 modulate a different set of genes and biological processes. Our genetic epistasis analysis supports our genomic finding and suggests that Ahr1 and Tye7 act independently to modulate hyphal growth in response to hypoxia. Furthermore, our genetic interaction experiments uncovered that Ahr1 and Tye7 repress the hypoxic filamentation growth via the Efg1 and Ras1/Cyr1 pathways, respectively.ConclusionIn sum, this investigation represents an informative resource toward the understanding of how hypoxia, the predominant condition inside the host, shapes the invasive filamentous growth of C. albicans.