Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal scaling of dopamine neuron firing and dopamine release by distinct ion channels shape behavior

Despite the widely known role of dopamine in reinforcement learning, how the patterns of dopamine release that are critical to the acquisition, performance, and extinction of conditioned responses are generated is poorly resolved. Here, we demonstrate that the coordinated actions of two ion channels, Kv4.3 and BKCa1.1, control the pattern of dopamine neuron firing and dopamine release on different time scales to regulate separate phases of reinforced behavior in mice. Inactivation of Kv4.3 in VTA dopamine neurons increases ex vivo pacemaker activity and excitability that is associated with increased in vivo ramping dynamics prior to lever press in a learned instrumental response paradigm. Loss of Kv4.3 enhances performance of the learned response and facilitates extinction. In contrast, loss of BKCa1.1 increases burst firing and phasic dopamine release that enhances learning of an instrumental response. Inactivation of BKCa1.1 enhances extinction burst lever pressing in early extinction training that is associated with increased reward prediction error signals. These data demonstrate that temporally distinct patterns of dopamine release are governed by the intrinsic regulators of the cell to shape behavior. Teaser We show that ion channels in midbrain dopamine neurons are critical for patterning action potential firing at the cell body and governing neurotransmitter release to regulate reinforcement learning.
Microtubule-mediated cell shape regulation contributes to efficient cell packing in Arabidopsis thaliana cotyledons

Recent advances have started to uncover the mechanisms involved in organ and cell shape regulation. However, organizational principles of epidermal cells in different tissues remain poorly understood. Here, we show that polygonal representations of cotyledon pavement cells (PCs) in Arabidopsis thaliana exhibit increasing irregularity in side lengths and internal vertex angles during early stages of development. While the shape of PCs in cotyledons is more complex than that of cells in the shoot apical meristem (SAM), the polygonal representations of these cells share similar irregularity of side length. Comparison of the surface cell area with the area of the regular polygons, having optimally spaced tri-cellular junctions, reveals suboptimal junction placement for coverage in cotyledons and SAM. We also found that cotyledons show increased packing density compared to the SAM, indicating that PCs forgo coverage of larger areas to potentially increase tissue stability. The identified shape irregularity and cell packing is associated with microtubule cytoskeleton. Our study provides a framework to analyze reasons and consequences of irregularity of polygonal shapes for biological as well as artificial shapes in larger organizational context. Summary We provide a polygonal cell representation in a tissue context and use it to draw conclusions about cell packing in epidermis of A. thaliana cotyledon.
Repetitive Sulfur Dioxide Exposure in Mice Models Post-Deployment Respiratory Syndrome.

Soldiers deployed to Iraq and Afghanistan have a higher prevalence of respiratory symptoms than non-deployed military personnel and some have been shown to have a constellation of findings on lung biopsy termed post-deployment respiratory syndrome (PDRS). Since many of the subjects in this cohort reported exposure to sulfur dioxide (SO2), we developed a model of repetitive exposure to SO2 in mice that phenocopies many aspects of PDRS, including adaptive immune activation, airway wall remodeling, and pulmonary vascular (PV) disease. Although abnormalities in small airways were not sufficient to alter lung mechanics, PV remodeling resulted in the development of pulmonary hypertension and reduced exercise tolerance in SO2 exposed mice. SO2 exposure led to increased formation of isolevuglandins (isoLGs) adducts and superoxide dismutase 2 (SOD2) acetylation in endothelial cells, which were attenuated by treatment with the isoLG scavenger 2-hydroxybenzylamine acetate (2-HOBA). In addition, 2-HOBA treatment or Siruin-3 overexpression in a transgenic mouse model prevented vascular remodeling following SO2 exposure. In summary, our results indicate that repetitive SO2 exposure recapitulates many aspects of PDRS and that oxidative stress appears to mediate PV remodeling in this model. Together, these findings provide new insights regarding the critical mechanisms underlying PDRS.
Are circadian amplitudes and periods correlated? A new twist in the story

Three parameters are important to characterize a circadian and in general any biological clock: period, phase and amplitude. While circadian periods have been shown to correlate with entrainment phases, and clock amplitude influences the phase response of an oscillator to pulse-like zeitgeber signals, the co-modulations of amplitude and periods, which we term twist, have not been studied in detail. In this paper we define two concepts: parametric twist refers to amplitude-period correlations arising in ensembles of self-sustained clocks in the absence of external inputs, and phase space twist refers to the co-modulation of an individual clock’s amplitude and period in response to external zeitgebers. Our findings show that twist influences the interaction of oscillators with the environment, facilitating entrainment, fastening recovery to pulse-like perturbations or modifying the response of an individual clock to coupling. This theoretical framework might be applied to understand the emerging properties of other oscillating systems.
Teaching CS-101 at the Dawn of ChatGPT

Recent news suggests that the advent of AI-generated coding tools signal the end of humans programming. This news should not, however, suggest that students not learn how to program but instead that instructors rethink how they teach programming. Math education has already addressed the challenge of teaching fluency when there is technology for basic tasks by having students use multiple representations, different approaches, and explanations of others' work to emphasize problem-solving, critical thinking, and communication while still teaching basic skills. These approaches can also be applied to computer science education, especially in an introductory course, and with the same benefits.
Two neuronal models of TDP-43 proteinopathy display reduced axonal translation, increased oxidative stress, and defective exocytosis

Amyotrophic lateral sclerosis (ALS) is a progressive, lethal neurodegenerative disease mostly affecting people around 50-60 years of age. TDP-43, a ubiquitously expressed RNA-binding protein involved in pre-mRNA splicing and controlling mRNA stability and translation, forms neuronal cytoplasmic inclusions in an overwhelming majority of ALS patients, of both sporadic and familial origin, a phenomenon referred to as TDP-43 proteinopathy. These cytoplasmic aggregates disrupt the subcellular transport and localization of mRNA. The axon, like dendrites, is a site of mRNA translation, permitting the local synthesis of selected proteins, both constitutively and in response to stimuli reaching the axon and presynaptic terminal. This is especially relevant in upper and lower motor neurons, whose axon spans long distances, likely accentuating their susceptibility to ALS-related noxae. In this work we have generated and characterized two models of TDP-43 proteinopathy, consisting of virtually pure populations of mouse cortical neurons expressing a human TDP-43 fusion protein, wt or mutant, which accumulates as cytoplasmic aggregates. Neurons expressing human TDP-43 exhibit a global impairment in axonal protein synthesis, an increase in oxidative stress, and defects in presynaptic function and electrical activity. These changes correlate with deregulation in the axonal levels of polysome-engaged mRNAs playing relevant roles in those processes. Our data support the emerging notion that deregulation of mRNA metabolism and of axonal mRNA transport may trigger the dying-back neuropathy that initiates motor neuron degeneration in ALS.
RealSeq2: a software integrated with UMI identification, error correction, and methylation modifications storing

High-throughput UMI technology sequencing is widely used in early tumor screening, detection, recurrence monitoring, etc. Detecting extremely low-frequency mutations is especially important for monitoring tumor recurrence, so high-precision data, as well as high-quality data, are required. We developed RealSeq2, a new integrated data-preprocessing software based on fastp and gencore, to achieve adapter removal, quality control, UMI identification, and generate consensus reads by clustering and error correction using multithreading in high-throughput next-generation sequencing background. RealSeq2 also supports methylation data of 5-methylcytosine bisulfite-free sequencing. RealSeq2 defined a new tag in SAM for storing methylation information, which is beneficial for co-identifying methylation sites and mutation sites for downstream analysis. RealSeq2 includes three submodules: ReadsProfiler, ReadsCleaner, and ReadsRecycler. In addition, the output format file (BAM or SAM) is universal for downstream analyses. RealSeq2 is the preferred upstream analysis software for the co-detection of ultra-low frequency mutations and bisulfite-free methylation data. The error profile provides data support for downstream analysis. Additionally, XM tags will become a standard protocol for recording methylation signals.
Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.
Marginated aberrant red blood cells induce pathologic vascular stress fluctuations in a computational model of hematologic disorders

Red blood cell (RBC) disorders affect billions worldwide. While alterations in the physical properties of aberrant RBCs and associated hemodynamic changes are readily observed, in conditions such as sickle cell disease and iron deficiency, RBC disorders can also be associated with vascular dysfunction. The mechanisms of vasculopathy in those diseases remain unclear and scant research has explored whether biophysical alterations of RBCs can directly affect vascular function. Here we hypothesize that the purely physical interactions between aberrant RBCs and endothelial cells, due to the margination of stiff aberrant RBCs, play a key role in this phenomenon for a range of disorders. This hypothesis is tested by direct simulations of a cellular scale computational model of blood flow in sickle cell disease, iron deficiency anemia, COVID-19, and spherocytosis. We characterize cell distributions for normal and aberrant RBC mixtures in straight and curved tubes, the latter to address issues of geometric complexity that arise in the microcirculation. In all cases aberrant RBCs strongly localize near the vessel walls (margination) due to contrasts in cell size, shape, and deformability from the normal cells. In the curved channel, the distribution of marginated cells is very heterogeneous, indicating a key role for vascular geometry. Finally, we characterize the shear stresses on the vessel walls; consistent with our hypothesis, the marginated aberrant cells generate large transient stress fluctuations due to the high velocity gradients induced by their near-wall motions. The anomalous stress fluctuations experienced by endothelial cells may be responsible for the observed vascular inflammation. Significance Statement A common and potentially life-threatening complication of blood cell disorders is inflammation and dysfunction of the vascular wall, for reasons that remain unclear. To address this issue, we explore a purely biophysical hypothesis involving red blood cells using detailed computational simulations. Our results show that red blood cells that are pathologically altered in cell shape, size, and stiffness, which occurs in various blood disorders, strongly marginate, residing primarily in the cell-free layer near blood vessel walls, generating large shear stress fluctuations at the vessel wall that may be responsible for endothelial damage and inflammation.
A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges

Significant progress has been made in symbolic music generation with the help of deep learning techniques. However, the tasks covered by symbolic music generation have not been well summarized, and the evolution of generative models for the specific music generation task has not been illustrated systematically. This paper attempts to provide a task-oriented survey of symbolic music generation based on deep learning techniques, covering most of the currently popular music generation tasks. The distinct models under the same task are set forth briefly and strung according to their motivations, basically in chronological order. Moreover, we summarize the common datasets suitable for various tasks, discuss the music representations and the evaluation methods, highlight current challenges in symbolic music generation, and finally point out potential future research directions.
Pipeline Design for Data Preparation for Social Media Analysis

In a data-driven culture, in which analytics applications are the main resources for supporting decision-making, the use of high-quality datasets is mandatory to minimize errors and risks. For this reason, data analysis tasks need to be preceded by a data preparation pipeline. The design of such a pipeline is not trivial: the data analyst must carefully choose the appropriate operations considering several aspects. This is often performed by adopting a trial-and-error approach that does not always lead to the most effective solution. In addition, extracting information from social media poses specific problems due to the need to consider only posts relevant for the analysis, for its dependence from the context being considered, for its multimedia contents, and for the risk of filtering out informative posts with automatic filters. In this article, we propose a systematic approach to support the design of pipelines that are able to effectively extract a relevant dataset for the goal of the analysis of data from social media. We provide a conceptual model for designing and annotating the data preparation pipeline with quality and performance information, thus providing the data analyst preliminary information on the expected quality of the resulting dataset in a context-aware manner. The generation of metadata related to the processing tasks has been recognized as essential for enabling data sharing and reusability. To this aim, the dataset resulting from the pipeline application is automatically annotated with provenance metadata to get a detailed description of all the activities performed by the pipeline on them. As a case study, we consider the design of a pipeline for creating datasets of images extracted from social media in order to analyze behavioural aspects during COVID-19.
Monitoring and maturation of cardiomyocyte differentiated from human induced pluripotent stem cells using a multi-functional tissue engineering platform

Background The use of human induced pluripotent stem cells (hiPSCs) is attracting attention as a potential personalized therapy for damaged myocardium, as they are accessible and compatible with the human body. The hiPSCs can be effectively differentiated into cardiomyocytes (CMs), which are essential for myocardial regeneration. However, current approaches have been unable to fully replicate the electro-mechanical functions of natural CMs. Methods Although several methods have been used to stimulate hiPSC-CMs for maturation, the ability to integrate multiple stimuli that influence myocardial cell function has been limited. To address this challenge, we have developed a multi-functional tissue engineering system for CMs that is based on a stretchable multielectrode array (SMEA). The SMEA is housed in a mini-incubator and is stretchable, durable and autoclavable. Results The system can independently control co-stimulation parameters of electrical and mechanical stimuli simultaneously. Furthermore, the system can monitor the status of the cells throughout their proliferation and differentiation of hiPSCs, as well as the stimulation of hiPSC-CMs, through electrochemical cell impedance spectroscopy. By applying co-stimulation, we have observed the enhanced maturation of hiPSC-CMs. Conclusions Our innovative system holds great potential as a tool for improving the culture and engineering of electrogenic cells with enhanced maturity.
Mitochondrial DNA Variations Modulate Alveolar Epithelial Mitochondrial Function and Oxidative Stress in Newborn Mice Exposed to Hyperoxia

Oxidative stress is an important contributor to bronchopulmonary dysplasia (BPD), a form of chronic lung disease that is the most common morbidity in very preterm infants. Mitochondrial functional differences due to inherited and acquired mutations influence the pathogenesis of disorders in which oxidative stress plays a critical role. We previously showed using mitochondrial-nuclear exchange (MNX) mice that mitochondrial DNA (mtDNA) variations modulate hyperoxia-induced lung injury severity in a model of BPD. In this study, we studied the effects of mtDNA variations on mitochondrial function including mitophagy in alveolar epithelial cells (AT2) from MNX mice. We also investigated oxidant and inflammatory stress as well as transcriptomic profiles in lung tissue in mice and expression of proteins such as PINK1, Parkin and SIRT3 in infants with BPD. Our results indicate that AT2 from mice with C57 mtDNA had decreased mitochondrial bioenergetic function and inner membrane potential, increased mitochondrial membrane permeability and were exposed to higher levels of oxidant stress during hyperoxia compared to AT2 from mice with C3H mtDNA. Lungs from hyperoxia-exposed mice with C57 mtDNA also had higher levels of pro-inflammatory cytokines compared to lungs from mice with C3H mtDNA. We also noted changes in KEGG pathways related to inflammation, PPAR and glutamatergic signaling, and mitophagy in mice with certain mito-nuclear combinations but not others. Mitophagy was decreased by hyperoxia in all mice strains, but to a greater degree in AT2 and neonatal mice lung fibroblasts from hyperoxia-exposed mice with C57 mtDNA compared to C3H mtDNA. Finally, mtDNA haplogroups vary with ethnicity, and Black infants with BPD had lower levels of PINK1, Parkin and SIRT3 expression in HUVEC at birth and tracheal aspirates at 28 days of life when compared to White infants with BPD. These results indicate that predisposition to neonatal lung injury may be modulated by variations in mtDNA and mito-nuclear interactions need to be investigated to discover novel pathogenic mechanisms for BPD.
Comprehensive Analysis of Molecular Characteristics, Clinical Signifificance, and Cancer Immune Interactions of Patients by Anoikis-Related Genes in LUAD Combined with Single-cell Data

Background Lung adenocarcinoma(LUAD) is the most prevalent subtype of lung cancer today. There is a close relationship between Anoikis related genes(ARGs) and tumor prognosis, drug susceptibility, and tumor microenvironment(TME). Method We calculated differential expression genes using downloaded Anoikis genes and selected genes of prognostic value. Consensus clustering analysis was used and characterized between different clusters. Differences between the different groups were also explored. Risk scores and Nomogram with predictive prognostic functions were established. Immune status and drug sensitivity were also assessed between different risk groups. Single-cell data were downloaded to compare the expression profiles of selected genes, and immunohistochemical results of selected genes were also downloaded to corroborate the reliability of the manuscript. Result Two clusters were identified on the basis of related gene expression. We analyzed the survival time, functional enrichment between the two groups and found significant differences between the two clusters. Significant relationships were found between the different clusters and clinical variables. group B had a significantly lower KM curve than group A, as well as a significant enrichment in multiple tumor functions. A risk score with prognostic value was established. The risk score was found to have a high predictive value for prognosis and was an independent prognostic factor. Combined with clinical variables, a Nomogram was established and found to be an accurate predictor of patient prognosis. There were significant differences in immune status between the different risk groups. Patients in the low-risk group were significantly better treated than those in the high-risk group. Finally single cell data confirmed the expression of the selected genes. Also, the immunohistochemical results helped us to confirm the selected genes have increased expression in tumor tissue. Conclusion In conclusion, this paper reveals the role of ARGs and immune status, drug susceptibility, and prediction of prognosis in LUAD. Also, an accurate prognostic prediction model was established based on genetic.
Extracting Local Reasoning Chains of Deep Neural Networks

We study how to explain the main steps of inference that a pre-trained deep neural net (DNN) relies on to produce predictions for a (sub)task and its data. This problem is related to network pruning and interpretable machine learning with the following highlighted diﬀer-ences: (1) ﬁne-tuning of any neurons/ﬁlters is forbidden; (2) we target a very high pruning rate, e.g., ≥ 95%, for better interpretability; (3) the interpretation is for the whole inference process on a few data of a task rather than for individual neurons/ﬁlters or a single sample. In this paper, we introduce NeuroChains to extract the local inference chains by optimizing diﬀerentiable sparse scores for the ﬁlters and layers, which reﬂects their importance in preserving the outputs on a few data drawn from a given (sub)task. Thereby, NeuroChains can extract an extremely small sub-network composed of critical ﬁlters exactly copied from the original pre-trained DNN by removing the ﬁlters/layers with small scores. For samples from the same class, we can then visualize the inference pathway in the pre-trained DNN by applying existing interpretation techniques to the retained ﬁlters and layers. It reveals how the inference process stitches and integrates the information layer by layer and ﬁlter by ﬁlter. We provide detailed and insightful case studies together with several quantitative analyses over thousands of trials to demonstrate the quality, sparsity, ﬁdelity and