Miracle-dz/UniFAR-FTUs huggingface dataset:
Innervation and Neuronal Control of the Mammalian Sinoatrial Node: A Comprehensive Atlas

Cardiac function is under exquisite intrinsic cardiac neural control. Neuroablative techniques to modulate control of cardiac function are currently being studied in patients, albeit with variable and sometimes deleterious results. Recognizing the major gaps in our understanding of cardiac neural control, we sought to evaluate neural regulation of impulse initiation in the sinoatrial node as an initial discovery step. Here, we report an in-depth, multi-scale structural and functional characterization of the innervation of the sinoatrial node (SAN) by the right atrial ganglionated plexus (RAGP) in porcine and human hearts. Combining intersectional strategies including tissue clearing, immunohistochemical and ultrastructural techniques, we have delineated a comprehensive neuroanatomic atlas of the RAGP-SAN complex. The RAGP shows significant phenotypic diversity of neurons while maintaining predominant cholinergic innervation. Cellular and tissue-level electrophysiologic mapping and ablation studies demonstrate interconnected ganglia with synaptic convergence within the RAGP to modulate SAN automaticity, atrioventricular (AV) conduction and left ventricular (LV) contractility. For the first time, we demonstrate that intrinsic cardiac neurons influence the pacemaking site in the heart. This provides an experimental demonstration of a discrete neuronal population controlling a specific geographic region of the heart (SAN) that can serve as a framework for further exploration of other parts of the intrinsic cardiac nervous system (ICNS) in mammalian hearts and for developing targeted therapies.
PhyKIT: a UNIX shell toolkit for processing and analyzing phylogenomic data

Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes, and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock, and collapsing bipartitions (internal branches) with low support. To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene-gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes; and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining, and deriving biological meaning from increasingly large phylogenomic datasets. PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT) and documentation including user tutorials are available online (https://jlsteenwyk.com/PhyKIT).
A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connec-tome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.
Regulation of retinal amacrine cell generation by miR-216b and Foxn3

The mammalian retina contains a complex mixture of different types of neurons. We find that the microRNA miR-216b is preferentially expressed in postmitotic retinal amacrine cells in the mouse retina, and expression of miR-216a/b and miR-217 in the retina depend in part on Ptf1a, a transcription factor required for amacrine cell differentiation. Surprisingly, ectopic expression of miR-216b, or the related miR-216a, can direct the formation of additional amacrine cells in the developing retina. In addition, we observe the loss of bipolar neurons in the retina after miR-216b expression. We identify the mRNA for the transcriptional regulator Foxn3 as a retinal target of miR-216b by Argonaute PAR-CLIP and reporter analysis. Inhibition of Foxn3 in the postnatal developing retina by RNAi also increases the formation of amacrine cells and reduces bipolar cell formation, while overexpression of Foxn3 inhibits amacrine cell formation prior to the expression of Ptf1a. Disruption of Foxn3 by CRISPR in embryonic retinal explants also reduces amacrine cell formation. Co-expression of Foxn3 can partially reverse the effects of ectopic miR-216b on retinal cell type formation. Our results identify Foxn3 as a novel regulator of interneuron formation in the developing retina and suggest that miR-216b likely regulates expression of Foxn3 and other genes in amacrine cells.
Neural substrates of verbal repetition deficits in primary progressive aphasia

This study examined the relationship between cortical thickness and performance on several verbal repetition tasks in patients with primary progressive aphasia (PPA) in order to test predictions generated by theoretical accounts of phonological working memory (PWM) that predict phonological content buffers in left posterior inferior frontal sulcus (pIFS) and supramarginal gyrus (SMG). Cortical surfaces were reconstructed from magnetic resonance imaging scans from 42 participants diagnosed with PPA. Cortical thickness was measured in a set of anatomical regions spanning the entire cerebral cortex. Correlation analyses were performed between cortical thickness and average score across three PWM related tasks: the Repetition subtest from the Western Aphasia Battery, a forward digit span task, and a backward digit span task. Significant correlations were found between average PWM score across tasks and cortical thickness in left SMG and left pIFS, in support of prior theoretical accounts of PWM. Exploratory whole-brain correlation analyses performed for each of the three behavioral tasks individually revealed a distinct set of positively-correlated regions for each task. Comparison of cortical thickness measures from different PPA subtypes to cortical thickness in age-matched controls further revealed unique patterns of atrophy in the different PPA subtypes.
Anesthetic Drug Concentration Rather Than Abrupt Behavioral Unresponsiveness Linearly Degrades Responses in the Rat Primary Auditory Cortex

Despite extensive knowledge of its molecular and cellular effects, how anesthesia affects sensory processing remains poorly understood. In particular, it remains unclear whether anesthesia modestly or robustly degrades activity in primary sensory regions, and whether such changes are linked to anesthesia drug concentration vs. behavioral unresponsiveness, since these are typically confounded. To address these questions, we employed slow gradual intravenous propofol anesthesia induction (from 100 to 900-1200 mcg/kg/min) together with auditory stimulation and intermittent assessment of behavioral responsiveness while recording neuronal spiking activity in the primary auditory cortex (PAC) of eight male rats. We found that all main components of neuronal activity including spontaneous firing rates, onset response magnitudes, onset response latencies, post-onset neuronal silence duration, and late-locking to 40Hz clicktrains, gradually deteriorated by 6-60% in a dose-dependent manner with increasing anesthesia levels, without showing abrupt changes around loss of righting reflex or other time-points. Thus, the dominant factor affecting PAC responses is the anesthesia drug concentration rather than any sudden, dichotomous behavioral state changes. Our findings recapitulate, within one experiment, a wide array of seemingly conflicting results in the literature that, depending on the precise definition of wakefulness (vigilant vs. drowsy) and anesthesia (just-hypnotic vs. deep surgical), report a spectrum of effects in primary regions ranging from minimal to dramatic differences.
Phylogenomic systematics of the spotted skunks (Carnivora, Mephitidae, Spilogale): Additional species diversity and Pleistocene climate change as a major driver of diversification

Four species of spotted skunks (Carnivora, Mephitidae, Spilogale) are currently recognized: Spilogale angustifrons, S. gracilis, S. putorius, and S. pygmaea. Understanding species boundaries within this group is critical for effective conservation given that regional populations or subspecies (e.g., S. p. interrupta) have experienced significant population declines. Further, there may be currently unrecognized diversity within this genus as some taxa (e.g., S. angustifrons) and geographic regions (e.g., Central America) never have been assessed using DNA sequence data. We analyzed species limits and diversification patterns in spotted skunks using multilocus nuclear (ultraconserved elements) and mitochondrial (whole mitogenomes and single gene analysis) data sets from broad geographic sampling representing all currently recognized species and subspecies. We found a high degree of genetic divergence among Spilogale that reflects seven distinct species and eight unique mitochondrial lineages. Initial divergence between S. pygmaea and all other Spilogale occurred 29 in the Early Pliocene (~ 5.0 million years ago) which was followed by subsequent diversification of the remaining Spilogale into an “eastern” and “western” lineage during the Early Pleistocene (~1.5 million years ago). These two lineages experienced temporally coincident patterns of diversification at ~0.66 and ~0.35 million years ago into two and ultimately three distinct evolutionary units, respectively. Diversification was confined almost entirely within the Pleistocene during a timeframe characterized by alternating glacial-interglacial cycles, with the origin of this diversity occurring in northeastern Mexico and the southwestern United States of America. Mitochondrial-nuclear discordance was recovered across three lineages in geographic regions consistent with secondary contact, including a distinct mitochondrial lineage confined to the Sonoran Desert. Our results have direct consequences for conservation of threatened populations, or species, as well as for our understanding of the evolution of delayed implantation in this enigmatic group of small carnivores.
RNF5 Regulation of RBBP4 Defines Acute Myeloid Leukemia Growth and Susceptibility to Histone Deacetylase Inhibitors

Acute myeloid leukemia (AML) remains incurable, largely due to its resistance to conventional treatments. Here, we found that increased expression and abundance of the ubiquitin ligase RNF5 contributes to AML development and survival. High RNF5 expression in AML patients correlated with poor prognosis. RNF5 inhibition decreased AML cell growth in culture and in vivo, and blocked development of MLL-AF9–driven leukemogenesis in mice, prolonging their survival. RNF5 inhibition led to transcriptional changes that overlapped with those seen upon HDAC1 inhibition. RNF5 induced the formation of K29 ubiquitin chains on the histone-binding protein RBBP4, promoting its recruitment and subsequent epigenetic regulation of genes involved in AML development and maintenance. Correspondingly, RNF5 or RBBP4 knockdown enhanced the sensitivity of AML cells to histone deacetylase (HDAC) inhibitors. Notably, low expression of RNF5 and HDAC coincided with a favorable prognosis. Our studies identified ERAD-independent role for RNF5, demonstrating that its control of RBBP4 constitutes an epigenetic pathway that drives AML while highlighting RNF5/RBBP4 as markers to stratify patients for treatment with HDAC inhibitors.
Robust Encoding of Abstract Rules by Distinct Neuronal Populations in Primate Visual Cortex

It is widely known that neural activity in sensory representations is modulated by cognitive factors such as attention, reward value and working memory. In such cases, sensory responses are found to reflect a selection of the specific sensory information needed to achieve behavioral goals. In contrast, more abstract behavioral constraints that do not involve stimulus selection, such as task rules, are thought to be encoded by neurons at later stages. We show that information about abstract rules is encoded by neurons in primate visual cortex in the absence of sensory stimulation. Furthermore, we show that rule information is greatest among neurons with the least visual activity and the weakest coupling to local neuronal networks. Our results identify rule-specific signals within a sensory representation and suggest that distinct mechanisms exist there for mapping rule information onto sensory guided decisions.
Possible changes in fidelity of DNA polymerase δ in ancestral mammals

DNA polymerase δ (polδ) is one of the major DNA polymerases that replicate chromosomal genomes in eukaryotes. Given the essential role of this protein, its phylogenetic tree was expected to reflect the relationship between taxa, like many other essential proteins. However, the tree of the catalytic subunit of polδ showed an unexpectedly strong heterogeneity among vertebrate lineages in evolutionary rate at the amino acid level, suggesting unusual amino acid substitutions specifically in the ancestral mammalian lineage. Structural and phylogenetic analyses were used to pinpoint where and when these amino acid substitutions occurred: around the 3′-5′ exonuclease domain in later mammal ancestry, after the split between monotremes and therians. The 3′-5′ exonuclease domain of this protein is known to have an impact on the fidelity of replication. Based on these observations, we explored the possibility that the amino acid substitutions we identified in polδ affected the mutation rate of entire chromosomal genomes in this time period.
Codon Usage Bias Levels Predict Taxonomic Identity and Genetic Composition

In this study, we investigate how an organism’s codon usage bias levels can serve as a predictor and classifier of various genomic and evolutionary features across the three kingdoms of life (archaea, bacteria, eukarya). We perform secondary analysis of existing genetic datasets to build several artificial intelligence (AI) and machine learning models trained on over 13,000 organisms that show it is possible to accurately predict an organism’s DNA type (nuclear, mitochondrial, chloroplast) and taxonomic identity simply using its genetic code (64 codon usage frequencies). By leveraging advanced AI and machine learning methods to accurately identify evolutionary origins and genetic composition from codon usage patterns, our study suggests that the genetic code can be utilized to train accurate machine learning classifiers of taxonomic and phylogenetic features. Our dataset and analyses are made publicly available on Github and the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Codon+usage) to facilitate open-source reproducibility and community engagement.
Identifying flow modules in ecological networks using Infomap

Analysing how species interact in modules is a fundamental problem in network ecology. Theory shows that a modular network structure can reveal underlying dynamic ecological and evolutionary processes, influence dynamics that operate on the network and affect the stability of the ecological system. Although many ecological networks describe flows, such as biomass flows in food webs or disease trans-mission, most modularity analyses have ignored network flows, which can hinder our understanding of the interplay between structure and dynamics. Here we present Infomap, an established method based on network flows to the field of ecological networks. Infomap is a flexible tool that can identify modules in virtually any type of ecological network and is particularly useful for directed, weighted and multilayer networks. We illustrate how Infomap works on all these network types. We also provide a fully documented repository with additional ecological examples. Finally, to help researchers analyse their networks with Infomap, we introduce the open source R package infomapecology. Analysing flow-based modularity is useful across ecology and transcends to other biological and non-biological disciplines. A dynamic approach for detecting modular structure has strong potential to provide new insights into the organisation of ecological networks.
Probabilistic Cause-of-disease Assignment using Case-control Diagnostic Tests: A Latent Variable Regression Approach

Optimal prevention and treatment strategies for a disease of multiple causes, such as pneumonia, must be informed by the population distribution of causes among cases, or cause-specific case fractions (CSCFs). CSCFs may further depend on additional explanatory variables. Existing methodological literature in disease etiology research does not fully address the regression problem, particularly under a case-control design. Based on multivariate binary non-gold-standard diagnostic data and additional covariate information, this paper proposes a novel and unified regression modeling framework for estimating covariate-dependent CSCF functions in case-control disease etiology studies. The model leverages critical control data for valid probabilistic cause assignment for cases.We derive an efficient Markov chain Monte Carlo algorithm for flexible posterior inference. We illustrate the inference of CSCF functions using extensive simulations and show that the proposed model produces less biased estimates and more valid inference of the overall CSCFs than analyses that omit covariates. A regression analysis of pediatric pneumonia data reveals the dependence of CSCFs upon season, age, HIV status and disease severity. The paper concludes with a brief discussion on model extensions that may further enhance the utility of the regression model in disease etiology research.
Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation

Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.
Combining multiple visual surveys to model the habitat of deep-diving cetaceans at the basin scale

Aim: Deep‐diving cetaceans are oceanic species exposed to multiple anthropogenic pressures including high intensity underwater noise, and knowledge of their distribution is crucial to manage their conservation. Due to intrinsic low densities, wide distribution ranges and limited presence at the sea surface, these species are rarely sighted. Pooling data from multiple visual surveys sharing a common linetransect methodology can increase sightings but requires accounting for heterogeneity in protocols and platforms. Location: North Atlantic Ocean and Mediterranean Sea. Time period: 1998 to 2015. Major taxa: Ziphiidae; Physeteriidae; Kogiidae. Methods: About 1,240,000 km of pooled effort provided 630 sightings of ziphiids, 836 of physeteriids and 106 of kogiids. For each taxon, we built a hierarchical model to estimate the effective strip width depending on observation conditions and survey types. We then modelled relative densities in a generalized additive modelling framework. Geographical predictions were limited to interpolations identified with a gap analysis of environmental space coverage. Results: Deeper areas of the North Atlantic gyre were mostly environmental extrapolation in the predictions, thereby highlighting gaps in sampling across the different surveys. For the three species groups, the highest relative densities were predicted along continental slopes, particularly in the western North Atlantic Ocean where the Gulf Stream creates dynamic frontal zones and eddies. Main conclusions: Pooling a large number of surveys provided the first basin‐wide models of distribution for deep‐diving cetaceans, including several data‐deficient taxa, across the North Atlantic Ocean and the Mediterranean Sea. These models can help the conservation of elusive and poorly known marine megafauna.
Joint Task Offloading and Payment Determination for Mobile Edge Computing: A Stable Matching Based Approach

In mobile edge computing (MEC), it is challenging to offload tasks to appropriate edge nodes due to the heterogeneity in both tasks and edge nodes. Most existing task offloading mechanisms mainly aim at optimizing the global system performance, e.g., social welfare, while ignoring the personal preferences of the individual tasks and edge nodes. However, in an open MEC system, a task offloading decision is prone to be unstable if edge nodes or task owners have incentives to deviate from the decided allocation, and seek for alternative choices to improve their own utilities. In addition, to win the competition, task owners may gradually adjust their payments, which brings new challenge in achieving the stability of the system. To address the above issues, this paper constructs a distributed many-to-many matching model to capture the interaction between mobile tasks and edge nodes, with the consideration of their diverse resource requirements and availabilities. Based on this, we design both distributed and centralized stable matching based algorithms to jointly offload the tasks to edge nodes, and determine their payments. We prove that the proposed mechanisms achieve several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed schemes can get optimal social welfare, when the considered tasks are homogeneous in terms of their resource requirements. Finally, we conduct simulations to validate the effectiveness of the proposed work.