Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Sample Preservation and Storage Methods for Metaproteomics Analysis of Intestinal Microbiomes

Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. ABSTRACT A critical step in studies of the intestinal microbiome using meta-omics approaches is the preservation of samples before analysis. Preservation is essential for approaches that measure gene expression, such as metaproteomics, which is used to identify and quantify proteins in microbiomes. Intestinal microbiome samples are typically stored by flash-freezing and storage at −80°C, but some experimental setups do not allow for immediate freezing of samples. In this study, we evaluated methods to preserve fecal microbiome samples for metaproteomics analyses when flash-freezing is not possible. We collected fecal samples from C57BL/6 mice and stored them for 1 and 4 weeks using the following methods: flash-freezing in liquid nitrogen, immersion in RNAlater, immersion in 95% ethanol, immersion in a RNAlater-like buffer, and combinations of these methods. After storage, we extracted protein and prepared peptides for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis to identify and quantify peptides and proteins. All samples produced highly similar metaproteomes, except for ethanol-preserved samples that were distinct from all other samples in terms of protein identifications and protein abundance profiles. Flash-freezing and RNAlater (or RNAlater-like treatments) produced metaproteomes that differed only slightly, with less than 0.7% of identified proteins differing in abundance. In contrast, ethanol preservation resulted in an average of 9.5% of the identified proteins differing in abundance between ethanol and the other treatments. Our results suggest that preservation at room temperature in RNAlater or an RNAlater-like solution performs as well as freezing for the preservation of intestinal microbiome samples before metaproteomics analyses. IMPORTANCE Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. A critical step for metaproteomics workflows is preserving samples before analysis because protein profiles are susceptible to fast changes in response to changes in environmental conditions (air exposure, temperature changes, etc.). This study evaluated the effects of different preservation treatments on the metaproteomes of intestinal microbiome samples. In contrast to prior work on preservation of fecal samples for metaproteomics analyses, we ensured that all steps of sample preservation were identical so that all differences could be attributed to the preservation method.
Probing the association between resting-state brain network dynamics and psychological resilience

This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, i.e., the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores, that were however very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices. Author Summary We tested the replicability and generalizability of a previously proposed negative association between dynamic brain network reconfigurations derived from multilayer modularity detection (node flexibility) and psychological resilience. Using multiband resting-state BOLD fMRI data and exploring several parcellation schemes, sliding window approaches, and temporal resolutions of the data, we could not replicate previously reported findings regarding the association between node flexibility and resilience. By extending this work to other measures of brain dynamics (node promiscuity, degree) we observe a rather inconsistent pattern of correlations with resilience, that strongly varies across analysis choices. We conclude that further research is needed to understand the network neuroscience basis of mental health and discuss several reasons that may account for the variability in results.
Lung Epithelial Regulation of BCL2 Related Protein A1 (BCL2A1) by Coronaviruses (SARS-CoV) and Type I Interferon Signaling

Highly pathogenic respiratory viruses such as 1918 influenza (HIN1) and coronavirus (SARS-CoV-2) induce significant lung injury with diffuse alveolar damage, capillary leak, and extensive cell death resulting in acute respiratory distress syndrome (ARDS). Direct effects of the virus, as well as host immune response such as proinflammatory cytokine production, contribute to programmed cell death or apoptosis. Alveolar lung epithelial type II (AT2) cells play a major role in the clearance of respiratory viruses, secretion of surfactant proteins and antimicrobial substances into the bronchoalveolar fluid as well as repair of lung injury. Gene expression in AT2 cells is regulated in a tissue and cell-specific manner and in a temporal fashion. The availability of tissue and cell-specific RNA datasets in Human Protein Atlas led to the identification of localized expression patterns of BCL-2 family members such as BCL2 related protein A1 (BCL2A1) in AT2 cells and immune cells of the lung. BCL2A1 expression was regulated by multiple stimuli including Toll-like receptor (TLR) ligands, interferons (IFNs), inflammatory cytokines, and inhibited by the steroid dexamethasone. In this study, regulation of BCL2A1 gene expression in human lung epithelial cells by several respiratory viruses and type I interferon signaling was investigated. SARS-CoV-2 infection significantly induced BCL2A1 expression in human lung epithelial cells within 24 hours that required the expression of Angiotensin-converting enzyme 2 (ACE2). BCL2A1 mRNA induction by SARS-CoV-2 was correlated with the induced expression of IFN-β and IFN-regulated transcription factor mRNA. BCL2A1 was induced by IFN-β treatment or by infection with influenza virus lacking the non-structural protein1(NS1) in NHBE cells. Furthermore, bioinformatics revealed that a subset of BCL-2 family members involved in the control of apoptosis and transcription such as BCL2A1, BCL2L14, BCL3, and BCL6 were regulated in the lung epithelial cells by coronaviruses and in the lung tissue samples of COVID-19 patients. Transcriptomic data also suggested that these genes were differentially regulated by the steroid drug dexamethasone.
SARS-CoV-2 Restructures the Host Chromatin Architecture

SARS-CoV-2 has made >190-million infections worldwide, thus it is pivotal to understand the viral impacts on host cells. Many viruses can significantly alter host chromatin1, but such roles of SARS-CoV-2 are largely unknown. Here, we characterized the three-dimensional (3D) genome architecture and epigenome landscapes in human cells after SARS-CoV-2 infection, revealing remarkable restructuring of host chromatin architecture. High-resolution Hi-C 3.0 uncovered widespread A compartmental weakening and A-B mixing, together with a global reduction of intra-TAD chromatin contacts. The cohesin complex, a central organizer of the 3D genome, was significantly depleted from intra-TAD regions, supporting that SARS-CoV-2 disrupts cohesin loop extrusion. Calibrated ChIP-Seq verified chromatin restructuring by SARS-CoV-2 that is particularly manifested by a pervasive reduction of euchromatin modifications. Built on the rewired 3D genome/epigenome maps, a modified activity-by-contact model2 highlights the transcriptional weakening of antiviral interferon response genes or virus sensors (e.g., DDX58) incurred by SARS-CoV-2. In contrast, pro-inflammatory genes (e.g. IL-6) high in severe infections were uniquely regulated by augmented H3K4me3 at their promoters. These findings illustrate how SARS-CoV-2 rewires host chromatin architecture to confer immunological gene deregulation, laying a foundation to characterize the long-term epigenomic impacts of this virus.
ASXL3 controls cortical neuron fate specification through extrinsic self-renewal pathways

During corticogenesis, transcription plasticity is fundamental to the restriction of neural progenitor cell (NPC) multipotency and production of cortical neuron heterogeneity. Human and mouse genetic studies have highlighted the role of Polycomb transcriptional regulation in this process. ASXL3, which encodes a component of the Polycomb repressive deubiquitination (PR-DUB) complex, has been identified as a high confidence autism spectrum disorder (ASD) risk gene. Genetic inactivation of Asxl3, in a mouse model that carries a clinically relevant ASXL3 frameshift (Asxl3fs) variant, disrupts lateral expansion of NPCs and delays cortical neuron differentiation. Single-cell RNA sequencing analysis implicates Notch signaling, which alters the composition of excitatory neurons and fidelity of cortical layer deposition. Our data provides a new link between extrinsic signaling cues and intrinsic epigenetic regulation that together control the timing of cell fate programs. Furthermore, transcriptomic analysis revealed dysregulation of other known ASD risk genes indicating that a convergent developmental pathway is affected. Collectively our work provides important insights about developmental mechanisms that contribute to ASD neuropathology.
Regulation of stem cell identity by miR-200a during spinal cord regeneration

Axolotls are an important model organism for multiple types of regeneration, including functional spinal cord regeneration. Remarkably, axolotls can repair their spinal cord after a small lesion injury and can also regenerate their entire tail following amputation. Several classical signaling pathways that are used during development are reactivated during regeneration, but how this is regulated remains a mystery. We have previously identified miR-200a as a key factor that promotes successful spinal cord regeneration. Here, using RNA-seq analysis, we discovered that the inhibition of miR-200a results in an upregulation of the classical mesodermal marker brachyury in spinal cord cells after injury. However, these cells still express the neural stem cell marker sox2. In vivo lineage tracing allowed us to determine that these cells can give rise to cells of both the neural and mesoderm lineage. Additionally, we found that miR-200a can directly regulate brachyury via a seed sequence in the 3’UTR of the gene. Our data indicate that miR-200a represses mesodermal cell fate after a small lesion injury in the spinal cord when only glial cells and neurons need to be replaced. Summary Statement After spinal cord injury, miR-200 fine-tunes expression levels brachyury and β-catenin to direct spinal cord stem into cells of the mesodermal or ectodermal lineage.
Sex determination gene transformer regulates the male-female difference in Drosophila fat storage via the adipokinetic hormone pathway

Sex differences in whole-body fat storage exist in many species. For example, Drosophila females store more fat than males. Yet, the mechanisms underlying this sex difference in fat storage remain incompletely understood. Here, we identify a key role for sex determination gene transformer (tra) in regulating the male-female difference in fat storage. Normally, a functional Tra protein is present only in females, where it promotes female sexual development. We show that loss of Tra in females reduced whole-body fat storage, whereas gain of Tra in males augmented fat storage. Tra’s role in promoting fat storage was largely due to its function in neurons, specifically the Adipokinetic hormone (Akh)-producing cells (APCs). Our analysis of Akh pathway regulation revealed a male bias in APC activity and Akh pathway function, where this sex-biased regulation influenced the sex difference in fat storage by limiting triglyceride accumulation in males. Importantly, Tra loss in females increased Akh pathway activity, and genetically manipulating the Akh pathway rescued Tra-dependent effects on fat storage. This identifies sex-specific regulation of Akh as one mechanism underlying the male-female difference in whole-body triglyceride levels, and provides important insight into the conserved mechanisms underlying sexual dimorphism in whole-body fat storage.
Forward planning driven by context-dependent conflict processing in anterior cingulate cortex

Forward planning is often essential to achieve goals over extended time periods. However, forward planning is typically computationally costly for the brain and should only be employed when necessary. The explicit calculation of how necessary forward planning will be, is in itself computationally costly. We therefore assumed that the brain generates a mapping from a particular situation to a proxy of planning value to make fast decisions about whether to use forward planning, or not. Moreover, since the state space of real world decision problems can be large, we hypothesized that such a mapping will rely on mechanisms that generalize sets of situations based on shared demand for planning. We tested this hypothesis in an fMRI study using a novel complex sequential task. Our results indicate that participants abstracted from the set of task features to more generalized control contexts that govern the balancing between forward planning and a simple response strategy. Strikingly, we found that correlations of conflict with response time and with activity in the dorsal anterior cingulate cortex were dependent on context. This context-dependency might reflect that the cognitive control system draws on category-based cognition, harnessing regularities in control demand across task space to generate control contexts that help reduce the complexity of control allocation decisions.
Analysis of genotype by environment interactions in a maize mapping population

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.
A machine learning model for screening of body fluid cytology smears

Introduction Body fluid cytology is one of the commonest investigations performed in indoor patients, both for diagnosis of suspected carcinoma as well as staging of known carcinoma. Carcinoma is diagnosed in body fluids by the pathologist through microscopic examination and searching for malignant epithelial cell clusters. The process of screening body fluid smears is a time consuming and error prone process. Aim We have attempted to construct a machine learning model which can screen body fluid cytology smears for malignant cells. Materials and methods MGG stained Ascitic / pleural fluid cytology smears were included from 21 cases (14 malignant, 07 benign) in this study. A total of 693 microphotographs were taken at 40x magnification at the same illumination and after correction of white balance. A Magnus Microphotography system was used for photography. The images were split into the training set (195 images), test set (120 images) and validation set (378 images). A machine learning model, a convolutional neural network, was developed in the Python programming language using the Keras deep learning library. The model was trained with the images of the training set. After completion of training, the model was evaluated on the test set of images. Results Evaluation of the model on the test set produced a sensitivity of 97.87%, specificity 85.26%, PPV 95.18%, NPV 93.10% In 06 images, the model has failed to detect singly scattered malignant cells/ clusters. 14 (3.7%) false positives was reported by the model. The machine learning model shows potential utility as a screening tool. However, it needs improvement in detecting singly scattered malignant cells and filtering inflammatory infiltrate.
Involuntary shifts of spatial attention contribute to distraction - Evidence from oscillatory alpha power and reaction time data

Imagine you are focusing on the traffic on a busy street to ride your bike safely when suddenly you hear the siren of an ambulance. This unexpected sound involuntarily captures your attention and interferes with ongoing performance. We tested whether this type of distraction involves a spatial shift of attention. We measured behavioral data and magnetoencephalographic alpha power during a crossmodal paradigm that combined an exogenous cueing task and a distraction task. In each trial, a task-irrelevant sound preceded a visual target (left or right). The sound was usually the same animal sound (i.e., standard sound). Rarely, it was replaced by an unexpected environmental sound (i.e., deviant sound). Fifty percent of the deviants occurred on the same side as the target, and 50% occurred on the opposite side. Participants responded to the location of the target. As expected, responses were slower to targets that followed a deviant compared to a standard. Crucially, this distraction effect was mitigated by the spatial relationship between the targets and the deviants: responses were faster when targets followed deviants on the same versus different side, indexing a spatial shift of attention. This was further corroborated by a posterior alpha power modulation that was higher in the hemisphere ipsilateral (vs. contralateral) to the location of the attention-capturing deviant. We suggest that this alpha power lateralization reflects a spatial attention bias. Overall, our data support the contention that spatial shifts of attention contribute to deviant distraction.
MatMorpher: A Morphing Operator for SVBRDFs

We present a novel morphing operator for spatially-varying bidirectional reflectance distribution functions. Our operator takes as input digital materials modeled using a set of 2D texture maps which control the typical parameters of a standard BRDF model. It also takes an interpolation map, defined over the same texture domain, which modulates the interpolation at each texel of the material. Our algorithm is based on a transport mechanism which continuously transforms the individual source maps into their destination counterparts in a feature-sensitive manner. The underlying non-rigid deformation is computed using an energy minimization over a transport grid and accounts for the user-selected dominant features present in the input materials. During this process, we carefully preserve details by mixing the material channels using a histogram-aware color blending combined with a normal reorientation. As a result, our method allows to explore large regions of the space of possible materials using exemplars as anchors and our interpolation scheme as a navigation mean. We also give details about our real time implementation, designed to map faithfully to the standard physically-based rendering workflow and letting users rule interactively the morphing process.
A Network Approach to the Study of the Dynamics of Risk Spillover in China’s Bond Market

Since 2018, the bond market has surpassed the stock market, becoming the biggest investment area in China’s security market, and the systemic risks of China’s bond market are of non-negligible importance. Based on daily interest rate data of representative bond categories, this study conducted a dynamic analysis based on generalized vector autoregressive volatility spillover variance decomposition, constructed a complex network, and adopted the minimum spanning tree method to clarify and analyze the risk propagation path between different bond types. It is found that the importance of each bond type is positively correlated with liquidity, transaction volume, and credit rating, and the inter-bank market is the most important market in the entire bond market, while interest rate bonds, bank bonds and urban investment bonds are important varieties with great systemic importance. In addition, the long-term trend of the dynamic spillover index of China’s bond market falls in line with the pace of the interest rate adjustments. To hold the bottom line of preventing financial systemic risks of China’s bond market, standard management, strict supervision, and timely regulation of the bond markets are required, and the structural entropy, as a useful indicator, also should be used in the risk management and monitoring.
Examining GDP Growth and Its Volatility: An Episodic Approach

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.