Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixture Margin Random-effects Copula Models for Inferring Temporally Conserved Microbial Co-variation Networks from Longitudinal Data

Longitudinal microbiome studies, in which data on a single subject is collected repeatedly over time, are becoming increasingly common in biomedical research. Such studies provide an opportunity to study the inherently dynamic nature of a microbiome in a way that cannot be done using cross-sectional studies. In this paper, we develop random-effects copula models with mixed zero-beta margins to identify biologically meaningful temporally conserved co-variation between two bacterial taxa, while accounting for the excessive zeros seen in 16S rRNA and metagenomic sequencing data. The model assumes a random-effects model for the dependence parameter in the copulas, which captures the conserved microbial co-variation while allowing for a time-specific dependence parameters. We develop a Monte Carlo EM algorithm for efficient estimation of model parameters and a corresponding Monte Carlo likelihood ratio test for the mean dependence parameter. Simulation studies show that our test controls the Type I error rate and provides an unbiased estimate of the mean dependence parameter. Additionally, we apply our method to a longitudinal pediatric cohort and identify changes in both local and global patterns of microbial co-variation networks in infants treated with antibiotics. Our analysis shows that the no antibiotics network is less dependent on individual taxon, thus making it more stable than the antibiotics network and more robust to both targeted and random attacks. Author summary Identification of co-variation between two microbes in microbial communities provides important insights into the community structure and stability. The commonly used measures of co-variation do not handle excessive zeros observed in the data and cannot be applied to longitudinal microbiome data directly. In this paper, we develop random-effects copula models with mixed zero-beta margins to identify biologically meaningful temporally conserved co-variation between two bacterial taxa, while accounting for the excessive zeros seen in 16S rRNA and metagenomic sequencing data. The model captures the conserved microbial co-variations while allowing for a time-specific dependence parameters. We develop an efficient Monte Carlo-based algorithm for parameter estimation and statistical inference. We analyze the data from a pediatric longitudinal cohort and identify changes in both local and global patterns of microbial co-variation networks in infants treated with antibiotics.
Improved imaging and preservation of lysosome dynamics using silver nanoparticle-enhanced fluorescence

The dynamics of living cells can be studied by live-cell fluorescence microscopy. However, this requires the use of excessive light energy to obtain good signal-to-noise ratio, which can then photobleach fluorochromes, and more worrisomely, lead to phototoxicity. Upon light excitation, noble metal nanoparticles such as silver nanoparticles (AgNPs) generate plasmons, which can then amplify excitation in direct proximity of the nanoparticle’s surface and couple to the oscillating dipole of nearby radiating fluorophores, modifying their rate of emission and thus, enhancing their fluorescence. Here, we show that AgNPs fed to cells to accumulate within lysosomes enhanced the fluorescence of lysosome-targeted Alexa488-conjugated dextran, BODIPY-cholesterol, and DQ-BSA. Moreover, AgNP increased the fluorescence of GFP fused to the cytosolic tail of LAMP1, showing that metal enhanced fluorescence can occur across the lysosomal membrane. The inclusion of AgNPs in lysosomes did not disturb lysosomal properties such as lysosomal pH, degradative capacity, autophagy and autophagic flux, and membrane integrity, though AgNP seemed to increase basal lysosome tubulation. Importantly, by using AgNP, we could track lysosome motility with reduced laser power without damaging and altering lysosome dynamics. Overall, AgNP-enhanced fluorescence may be a useful tool to study the dynamics of the endo-lysosomal pathway while minimizing phototoxicity.
Explainable Machine Learning Approach to Predict and Explain the Relationship between Task-based fMRI and Individual Differences in Cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based fMRI. Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n=3,989). We compared nine predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and OLS multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than non-linear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study (Sripada et al. 2020) and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
Cytoplasmic processing of human transfer RNAs

Biogenesis of different classes of eukaryotic RNAs proceeds via different pathways that require strict spatiotemporal resolution. The intracellular organization has evolved to provide order for RNA processing to coordinate different maturation steps with specific enzymatic reaction. In higher eukaryotes, processing of transfer RNAs (tRNAs) is postulated to be almost entirely intranuclear, while in lower eukaryotes like yeast, tRNA maturation is both nuclear and cytoplasmic. Here, we show that tRNA processing is largely cytosolic event in human cells. After transcription, unprocessed precursors of tRNAs (pre-tRNAs) bound by La protein are exported from the nucleus into the cytoplasm. Using cell fractionation analysis and pre-tRNA-specific fluorescence in situ hybridization (FISH) protocol, we show tRNA splicing/ligation and end-processing takes place in the cytoplasm where majority of processing enzymes is also located. We propose here a model where processing of intron-less and intron-containing pre-tRNAs is cytoplasmic similarly to the observed in lower eukaryotes, although tRNA splicing precedes 5’- and 3’-end processing in human cells unlike in yeast.
ATP6AP2-to-MMP14, a key pathway for osteoblast to osteocyte transition

Osteocytes, derived from osteoblast (OB)-lineage, occupy lacunae within bone matrix and exhibit unique morphology with dendrite-like projections to form an inter-connected network. Such a network is essential for osteocytes to monitor and orchestrate bone homeostasis. Thus, it is of considerable interest to investigate how osteocytes are formed and how they built the network. Here we provide evidence for ATP6AP2, an accessory subunit of V-ATPase, in OB-lineage cells to be critical for OB-to-osteocyte transition, and osteocyte distribution, maturation, and morphogenesis. Mice (ATP6AP2Ocn-Cre) that selectively deplete ATP6AP2 in OB-lineage cells results in altered osteocyte distribution and morphology, impaired osteocyte maturation and dendrite-like processes, increased osteocyte cell death and cortical woven bone formation. Further mechanistic studies identify MMP14 (matrix metalloproteinase-14) as a critical downstream of ATP6AP2 for osteocyte differentiation. ATP6AP2 interacts with MMP14 and promotes MMP14 surface distribution largely in immature- or osteoid-osteocytes, where this pathway regulates bone matrix remodeling and osteocyte differentiation. Expression of MMP14 into ATP6AP2 knock out OB-lineage cells in the mouse cortical bone could diminish the deficits in osteocyte maturation, dendrite-like process formation, and survival. These results thus demonstrate an un-recognized function of ATP6AP2 in promoting OB-to-osteocyte transition and uncover a pathway from ATP6AP2-to-MMP14 in osteocyte differentiation.
Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference

Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY, a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint-probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, such as the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and predicts novel regulatory networks from scRNA-seq and scATAC-seq datasets given partial ground-truth labels. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an open-source implementation of DELAY at https://github.com/calebclayreagor/DELAY.
Building a novel nuclear-organelle genomic framework for the fever tree ( Cinchona pubescens Vahl) through short and long-read DNA data assemblies

Background: The Andean fever tree ( Cinchona L.; Rubiaceae) is the iconic source of bioactive quinine alkaloids which have been key to treating malaria for centuries. In particular, C. pubescens Vahl has been an important source of income for several countries in its native range in north-western South America. However, the genomic resources required to place Cinchona species in the tree of life and to explore the evolution and biosynthesis of alkaloids are meagre. Findings: Using a combination of ~120 Gb of long sequencing reads derived from the Oxford Nanopore PromethION platform and 142 Gb of short read Illumina data, we address this gap by providing the first highly contiguous nuclear and organelle genome assemblies and their corresponding annotations. Our nuclear genome assembly consists of 603 scaffolds comprising a total length of 903 Mb, representing ~85% of the genome size (1.1 Gb/1C). This draft genome sequence was complemented by annotating 72,305 CDSs using a combination of de novo and reference-based transcriptome assemblies. Completeness analysis revealed that our assembly is highly complete, displaying 83% of the BUSCO gene set, and a small fraction of genes (4.6%) classified as fragmented. We demonstrate the utility of these novel genomic resources by placing C. pubescens in the Gentianales order using plastid and nuclear datasets. Conclusions: Our study provides the first genomic resource for C. pubescens thus opening new research avenues, including the unravelling of the gene toolkit for alkaloid biosynthesis in the fever tree.
Fasciclin II signals new synapse formation through amyloid Fasciclin II signals new synapse formation through amyloid precursor protein and the scaffolding protein dX11/Mint precursor protein and the scaffolding protein dX11/Mint

Cell adhesion molecules (CAMs) have been universally recognized for their essential roles during synapse remodeling. However, the downstream pathways activated by CAMs have remained mostly unknown. Here, we used the Drosophila larval neuromuscular junction toinvestigatethepathwaysactivatedbyFasciclinII(FasII),atransmembraneCAMoftheIgsuperfamily,duringsynapseremodeling.WeshowthattheabilityofFasIItostimulateortopreventsynapseformationdependsonthesymmetryoftransmembraneFasIIlevelsinthepresynapticandpostsynapticcellandrequiresthepresenceoftheflyhomologofamyloidprecursorprotein(APPL). Inturn,APPLisregulatedbydirectinteractionswiththePDZ(postsynapticdensity-95/Discslarge/zonaoccludens-1)-containingproteindX11/Mint/Lin-10,whichalsoregulatessynapseexpansiondownstreamofFasII.TheseresultsprovideanovelmechanismbywhichcelladhesionmoleculesareregulatedandprovidefreshinsightsintothenormaloperationofAPPduringsynapsedevelopment.
How do personality traits influence the experience of positive emotions?

Neuroticism is a personality trait known to be associated with negative emotions (Tellegan, 1985), but little is known about how it affects the experience of positive emotions, and more specifically, hope. Positive emotions can have long lasting effects on well-being, by broadening thought-action repertoires and undoing lingering negative emotions (Frederickson, 1998). Cognitive strategies strongly influence state positive emotions in individuals low in neuroticism, but have no effect on those with high neuroticism (Ng & Diener, 2009). We expected individuals high in neuroticism would experience a smaller increase in state hope following hope emotion induction. Participants completed an online survey involving a personality test and trait positive and negative emotion questionnaires. Prior to and following being randomly assigned to either the hope (n = 147) or nurturant love (n = 142) induction procedure, participants completed questionnaires on state positive and negative emotions. The results revealed the hope induction procedures of autobiographical recall and imagery were not effective at inducing state hope, but increased state positive emotions and decreased state negative emotions. Neuroticism was negatively correlated with trait hope and trait positive emotions and positively correlated with trait negative emotions. Exploratory analyses of control questions regarding how often participants thought of hopeful memories and whether they believe engaging in this daily to be beneficial were also performed. It was concluded that neuroticism is negatively associated with the experience of trait and state positive emotions, but not state hope. The limitations of the study design and future research are also discussed.
Design methodology for very high insulation voltage capabilities power transmission function for IGBT gate drivers based on a virtual prototyping tool

For the operation of the semiconductor components in medium voltage converter or multilevel converter applications (where the DC bus voltage up to several ten kilovolts), their gate driver systems require very high galvanic insulation voltage capabilities. Hence, this study details a design methodology to optimise a power supply function for insulated gate bipolar transistor gate drivers with high insulated voltage capabilities. To achieve this, a dc-to-dc full-bridge series–series resonant converter with a high air gap pot core planar transformer is selected. This air gap length and the dielectric material define the high insulation voltage capability. The objective of the study is to optimise the geometric elements of the transformer and its associated electrical components in the proposed converter. Magnetic and electrostatic modellings are provided. Maximise the converter efficiency (η con) is the main objective of the proposed optimisation design. Thus, the optimisation results are proposed: the converter efficiency (η con) as function of converter output power (P out). Finally, to validate the authors’ proposed methodology, the experimental results are compared with the simulation ones. Then, the results of an improved optimisation design are provided.
A Q-band high gain and low noise variable gain amplifier using dual gate HEMTs

A Q-band high gain and low noise Variable Gain Amplifier (VGA) using dual gate AlGaAs/InGaAs pseudomorphic HEMTs has been developed. The dual gate HEMT can be fabricated using the same process as the single gate HEMT with a gate length of 0.15 /spl mu/m. The Q-band VGA consists of a 1-stage low noise amplifier MMIC using a single gate HEMT and a 2-stage VGA MMIC using dual gate HEMTs. The VGA has a gain of more than 20 dB from 41 GHz to 52 GHz. A gain control range of more than 30 dB is obtained in the same frequency range. A minimum noise figure of 1.8 dB with an associated gain of 22 dB is achieved at 43 GHz when biased for a low noise figure. This performance is comparable with the best data ever reported for LNAs at Q-band including both GaAs based HEMTs and InP based HEMTs.
Remote Sensing Survey of Altiplano-Puna Volcanic Complex Rocks and Minerals for Planetary Analog Use

The Altiplano-Puna Volcanic Complex (APVC) of the Central Andes is an arid region with extensive volcanism, possessing various geological features comparable to those of other solar system objects. The unique features of the APVC, e.g., hydrothermal fields and evaporite salars, have been used as planetary analogs before, but the complexity of the APVC presents a wealth of opportunities for more analog studies that have not been exploited previously. Motivated by the potential of using the APVC as an analog of the volcanic terrains of solar system objects, we mapped the mineralogy and silica content of the APVC up to ~100,000 km2 in northern Chile based on a combination of remote sensing data resembling those of the Moon and Mars. The band ratio indices of Landsat 8 Operational Land Imager multispectral images and mineral classifications based on spectral hourglass approach using Earth Observing-1 Hyperion hyperspectral images (both in the visible to shortwave infrared wavelengths) were used to map iron-bearing and alteration minerals. We also used Hyperion imagery to detect feldspar spectral signatures and demonstrated that feldspar minerals can be detected on non-anorthosites, which may influence interpretations of feldspar spectral signatures on Mars. From the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset, we derived the silica percentage of non-evaporite rocks within errors of approximately 2–3 wt. % SiO2 for those in the 60–70 wt. % range (about 8 wt.% errors for the 50–60 wt.% range). Based on an integrated assessment of the three datasets, we highlighted three regions of particular interest worthy of further field investigation. We also evaluated the benefits and limitations of all three remote sensing methods for mapping key minerals and capturing rock diversity, based on available samples and existing geological maps.
DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the depth code) derived from deep neural networks has been employed in the visual-only or visual-inertial simultaneous localization and mapping (SLAM) systems, which achieve promising performances on both camera motion and local dense geometry estimations from monocular images. However, the existing visual-inertial SLAM systems combined with depth codes are either built on a filter-based SLAM framework, which can only update poses and maps in a relatively small local time window, or based on a loosely-coupled framework, while the prior geometric constraints from the depth estimation network have not been employed for boosting the state estimation. To well address these drawbacks, we propose DiT-SLAM, a novel real-time Dense visual-inertial SLAM with implicit depth representation and Tightly-coupled graph optimization. Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. Meanwhile, we propose a light-weight monocular depth estimation and completion network, which is combined with attention mechanisms and the conditional variational auto-encoder (CVAE) to predict the uncertainty-aware dense depth maps from more low-dimensional codes. Furthermore, a robust point sampling strategy introducing the spatial distribution of 2D feature points is also proposed to provide geometric constraints in the tightly-coupled optimization, especially for textureless or featureless cases in indoor environments. We evaluate our system on open benchmarks. The proposed methods achieve better performances on both the dense depth estimation and the trajectory estimation compared to the baseline and other systems.
DDDT_A_355059 1255..1272

Plant-derived sesquiterpene lactones are promising natural sources for the discovery of anti-cancer drugs. As an extensively studied sesquiterpene lactone, the tumor suppression effect of parthenolide (PTL) has been clarified by targeting a number of prominent signaling pathways and key protein regulators in carcinogenesis. Notably, PTL was also the first small molecule reported to eradicate cancer stem cells. Nevertheless, the clinical application of PTL as an antitumor agent remains limited, owing to some disadvantages such as low water solubility and poor bioavailability. Thus, nanomedicine has attracted much interest because of its great potential for transporting poorly soluble drugs to desired body sites. In view of the significant advantages over their free smallmolecule counterparts, nanoparticle delivery systems appear to be a potential solution for addressing the delivery of hydrophobic drugs, including PTL. In this review, we summarized the key anticancer mechanisms underlined by PTL as well as engineered PTL nanoparticles synthesized to date. Therefore, PTL nanoformulations could be an alternative strategy to maximize the therapeutic value of PTL.