A functional mechanism for a non-coding variant near AGTR2 associated with risk for preterm birth.

BACKGROUND: Preterm birth (PTB), defined as delivery before 37 gestational weeks, imposes significant public health burdens. A recent maternal genome-wide association study of spontaneous PTB identified a noncoding locus near the angiotensin II receptor type 2 (AGTR2) gene. Genotype-Tissue Expression data revealed that alleles associated with decreased AGTR2 expression in the uterus were linked to an increased risk of PTB and shortened gestational duration. We hypothesized that a causative variant in this locus modifies AGTR2 expression by altering transcription factor (TF) binding. METHODS: To investigate this hypothesis, we performed bioinformatics analyses and functional characterizations at the implicated locus. Potential causal single nucleotide polymorphisms (SNPs) were prioritized, and allele-dependent binding of TFs was predicted. Reporter assays were employed to assess the enhancer activity of the top PTB-associated non-coding variant, rs7889204, and its impact on TF binding. RESULTS: Our analyses revealed that rs7889204, a top PTB-associated non-coding genetic variant is one of the strongest eQTLs for the AGTR2 gene in uterine tissue samples. We observed differential binding of CEBPB (CCAAT enhancer binding protein beta) and HOXA10 (homeobox A10) to the alleles of rs7889204. Reporter assays demonstrated decreased enhancer activity for the rs7889204 risk "C" allele. CONCLUSION: Collectively, these results demonstrate that decreased AGTR2 expression caused by reduced transcription factor binding increases the risk for PTB and suggest that enhancing AGTR2 activity may be a preventative measure in reducing PTB risk.

Brillouin frequency shift error estimation formula for distributed optical fiber sensing technology based on Brillouin scattering.

In this work, the problems in the existing Brillouin frequency shift (BFS) error estimation formulas for distributed optical fiber sensing technology based on Brillouin scattering are discussed. Based on the analysis, a new, to the best of our knowledge, BFS error estimation formula is proposed. To validate the proposed formula, a large number of Brillouin spectra with different frequency sweep spans, signal-to-noise ratios, linewidths, and frequency steps are numerically generated, and at the same time, Brillouin spectra with different values of incident light pulse widths and frequency sweep spans are measured with a Brillouin optical time domain reflectometer. Based on those Brillouin spectra, the errors of the proposed formula and existing formulas are systematically compared. The results reveal that the proposed formula generally has a higher accuracy than the existing typical formulas, especially when the frequency sweep span or incident light pulse width is large. Therefore, it has a much wider application range.

Eutherian-Specific Gene TRIML2 Attenuates Inflammation in the Evolution of Placentation.

Evolution of highly invasive placentation in the stem lineage of eutherians and subsequent extension of pregnancy set eutherians apart from other mammals, that is, marsupials with short-lived placentas, and oviparous monotremes. Recent studies suggest that eutherian implantation evolved from marsupial attachment reaction, an inflammatory process induced by the direct contact of fetal placenta with maternal endometrium after the breakdown of the shell coat, and shortly before the onset of parturition. Unique to eutherians, a dramatic downregulation of inflammation after implantation prevents the onset of premature parturition, and is critical for the maintenance of gestation. This downregulation likely involved evolutionary changes on maternal as well as fetal/placental side. Tripartite-motif family-like2 (TRIML2) only exists in eutherian genomes and shows preferential expression in preimplantation embryos, and trophoblast-derived structures, such as chorion and placental disc. Comparative genomic evidence supports that TRIML2 originated from a gene duplication event in the stem lineage of Eutheria that also gave rise to eutherian TRIML1. Compared with TRIML1, TRIML2 lost the catalytic RING domain of E3 ligase. However, only TRIML2 is induced in human choriocarcinoma cell line JEG3 with poly(I:C) treatment to simulate inflammation during viral infection. Its knockdown increases the production of proinflammatory cytokines and reduces trophoblast survival during poly(I:C) stimulation, while its overexpression reduces proinflammatory cytokine production, supporting TRIML2's role as a regulatory inhibitor of the inflammatory pathways in trophoblasts. TRIML2's potential virus-interacting PRY/SPRY domain shows significant signature of selection, suggesting its contribution to the evolution of eutherian-specific inflammation regulation during placentation.

Anthropoid primate-specific retroviral element THE1B controls expression of CRH in placenta and alters gestation length.

Pregnancy and parturition are intricately regulated to ensure successful reproductive outcomes. However, the factors that control gestational length in humans and other anthropoid primates remain poorly defined. Here, we show the endogenous retroviral long terminal repeat transposon-like human element 1B (THE1B) selectively controls placental expression of corticotropin-releasing hormone (CRH) that, in turn, influences gestational length and birth timing. Placental expression of CRH and subsequently prolonged gestational length were found in two independent strains of transgenic mice carrying a 180-kb human bacterial artificial chromosome (BAC) DNA that contained the full length of CRH and extended flanking regions, including THE1B. Restricted deletion of THE1B silenced placental CRH expression and normalized birth timing in these transgenic lines. Furthermore, we revealed an interaction at the 5' insertion site of THE1B with distal-less homeobox 3 (DLX3), a transcription factor expressed in placenta. Together, these findings suggest that retroviral insertion of THE1B into the anthropoid primate genome may have initiated expression of CRH in placental syncytiotrophoblasts via DLX3 and that this placental CRH is sufficient to alter the timing of birth.

Immunosuppressive CD71+ erythroid cells compromise neonatal host defence against infection.

Newborn infants are highly susceptible to infection. This defect in host defence has generally been ascribed to the immaturity of neonatal immune cells; however, the degree of hyporesponsiveness is highly variable and depends on the stimulation conditions. These discordant responses illustrate the need for a more unified explanation for why immunity is compromised in neonates. Here we show that physiologically enriched CD71(+) erythroid cells in neonatal mice and human cord blood have distinctive immunosuppressive properties. The production of innate immune protective cytokines by adult cells is diminished after transfer to neonatal mice or after co-culture with neonatal splenocytes. Neonatal CD71(+) cells express the enzyme arginase-2, and arginase activity is essential for the immunosuppressive properties of these cells because molecular inhibition of this enzyme or supplementation with L-arginine overrides immunosuppression. In addition, the ablation of CD71(+) cells in neonatal mice, or the decline in number of these cells as postnatal development progresses parallels the loss of suppression, and restored resistance to the perinatal pathogens Listeria monocytogenes and Escherichia coli. However, CD71(+) cell-mediated susceptibility to infection is counterbalanced by CD71(+) cell-mediated protection against aberrant immune cell activation in the intestine, where colonization with commensal microorganisms occurs swiftly after parturition. Conversely, circumventing such colonization by using antimicrobials or gnotobiotic germ-free mice overrides these protective benefits. Thus, CD71(+) cells quench the excessive inflammation induced by abrupt colonization with commensal microorganisms after parturition. This finding challenges the idea that the susceptibility of neonates to infection reflects immune-cell-intrinsic defects and instead highlights processes that are developmentally more essential and inadvertently mitigate innate immune protection. We anticipate that these results will spark renewed investigation into the need for immunosuppression in neonates, as well as improved strategies for augmenting host defence in this vulnerable population.

Event-related prefrontal activations during online video game playing are modulated by game mechanics, physiological arousal and the amount of daily playing.

There is a trend to study human brain functions in ecological contexts and in relation to human factors. In this study, functional near-infrared spectroscopy (fNIRS) was used to record real-time prefrontal activities in 42 male university student habitual video game players when they played a round of multiplayer online battle arena game, League of Legends. A content-based event coding approach was used to analyze regional activations in relation to event type, physiological arousal indexed by heart rate (HR) change, and individual characteristics of the player. Game events Slay and Slain were found to be associated with similar HR and prefrontal responses before the event onset, but differential responses after the event onset. Ventrolateral prefrontal cortex (VLPFC) activation preceding the Slay onset correlated positively with HR change, whereas activations in dorsolateral prefrontal cortex (DLPFC) and rostral frontal pole area (FPAr) preceding the Slain onset were predicted by self-reported hours of weekly playing (HoWP). Together, these results provide empirical evidence to support the notion that event-related regional prefrontal activations during online video game playing are shaped by game mechanics, in-game dynamics of physiological arousal and individual characteristics the players.

ROBUST QUANTIFICATION OF PERCENT EMPHYSEMA ON CT VIA DOMAIN ATTENTION: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.

Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Although the domain shifts in different CT scanners are subtle compared to shifts existing in other modalities (e.g., MRI) or cross-modality, emphysema is highly sensitive to it. Such subtle difference limits the application of general domain adaptation methods, such as image translation-based methods, as the contrast difference is too subtle to be distinguished. Existing studies have explored several directions to tackle this challenge, including density correction, noise filtering, regression, hidden Markov measure field (HMMF) model-based segmentation, and volume-adjusted lung density. Despite some promising results, previous studies either required a tedious workflow or eliminated opportunities for downstream emphysema subtyping, limiting efficient adaptation on a large-scale study. To alleviate this dilemma, we developed an end-to-end deep learning framework based on an existing HMMF segmentation framework. We first demonstrate that a regular UNet cannot replicate the existing HMMF results because of the lack of scanner priors. We then design a novel domain attention block, a simple yet efficient cross-modal block to fuse image visual features with quantitative scanner priors (a sequence), which significantly improves the results.

A new strategy based on a cascade amplification strategy biosensor for on-site eDNA detection and outbreak warning of crown-of-thorns starfish.

Population outbreaks of the crown-of-thorns starfish (COTS) seriously threaten the sustainability of coral reef ecosystems. However, traditional ecological monitoring techniques cannot provide early warning before the outbreaks, thus preventing timely intervention. Therefore, there is an urgent need for a more accurate and faster technology to predict the outbreaks of COTS. In this work, we developed an electrochemical biosensor based on a programmed catalytic hairpin assembly (CHA) and hybridization chain reaction (HCR) cyclic amplification strategy for sensitive and selective detection of COTS environmental DNA (eDNA) in water bodies. This biosensor exhibited excellent electrochemical characteristics, including a low limit of detection (LOD = 18.4 fM), low limit of quantification (LOQ = 41.1 fM), and wide linear range (50 fM - 10 nM). The biosensing technology successfully allowed the detection of COTS eDNA in the aquarium environment, and the results also demonstrated a significant correlation between eDNA concentration and COTS number (r = 0.990; P < 0.001). The reliability and accuracy of the biosensor results have been further validated through comparison with digital droplet PCR (ddPCR). Moreover, the applicability and accuracy of the biosensor were reconfirmed in field tests at the COTS outbreak site in the South China Sea, which has shown potential application in dynamically monitoring the larvae before the COTS outbreak. Therefore, this efficient electrochemical biosensing technology offers a new solution for on-site monitoring and early warning of the COTS outbreak.

Heart Rate Variability during Online Video Game Playing in Habitual Gamers: Effects of Internet Addiction Scale, Ranking Score and Gaming Performance.

Previous studies have demonstrated that individuals with internet gaming disorder (IGD) display abnormal autonomic activities at rest and during gameplay. Here, we examined whether and how in-game autonomic activity is modulated by human characteristics and behavioral performance of the player. We measured heart rate variability (HRV) in 42 male university student habitual gamers (HGs) when they played a round of League of Legends game online. Short-term HRV indices measured in early, middle and late phases of the game were compared between the players at high risk of developing IGD and those at low risk, as assessed by the revised Chen Internet addiction scale (CIAS-R). Multiple linear regression (MLR) was used to identify significant predictors of HRV measured over the whole gameplay period (WG), among CIAS-R, ranking score, hours of weekly playing and selected in-game performance parameters. The high-risk players showed a significantly higher low-frequency power/high-frequency power ratio (LF/HF) relative to the low-risk players, regardless of game phase. MLR analysis revealed that LF/HF measured in WG was predicted by, and only by, CIAS-R. The HRV indicators of sympathetic activity were found to be predicted only by the number of slain in WG (NSlain), and the indicators of parasympathetic activity were predicted by both CIAS-R and NSlain. Collectively, the results demonstrated that risk of developing IGD is associated with dysregulated autonomic balance during gameplay, and in-game autonomic activities are modulated by complex interactions among personal attributes and in-game behavioral performance of the player, as well as situational factors embedded in game mechanics.

T lymphocyte characteristics and immune repertoires in the epicardial adipose tissue of heart failure patients.

Background: Epicardial adipose tissue (EAT) acts as an active immune organ and plays a critical role in the pathogenesis of heart failure (HF). However, the characteristics of immune cells in EAT of HF patients have rarely been elucidated. Methods: To identify key immune cells in EAT, an integrated bioinformatics analysis was performed on public datasets. EAT samples with paired subcutaneous adipose tissue (SAT), heart, and peripheral blood samples from HF patients were collected in validation experiments. T cell receptor (TCR) repertoire was assessed by high-throughput sequencing. The phenotypic characteristics and key effector molecules of T lymphocytes in EAT were assessed by flow cytometry and histological staining. Results: Compared with SAT, EAT was enriched for immune activation-related genes and T lymphocytes. Compared with EAT from the controls, activation of T lymphocytes was more pronounced in EAT from HF patients. T lymphocytes in EAT of HF patients were enriched by highly expanded clonotypes and had greater TCR clonotype sharing with cardiac tissue relative to SAT. Experiments confirmed the abundance of IFN-γ+ effector memory T lymphocytes (TEM) in EAT of HF patients. CCL5 and GZMK were confirmed to be associated with T lymphocytes in EAT of HF patients. Conclusion: EAT of HF patients was characterized by pronounced immune activation of clonally expanded IFN-γ+ TEM and a generally higher degree of TCR clonotypes sharing with paired cardiac tissue.

Fully transformer network for skin lesion analysis.

Automatic skin lesion analysis in terms of skin lesion segmentation and disease classification is of great importance. However, these two tasks are challenging as skin lesion images of multi-ethnic population are collected using various scanners in multiple international medical institutes. To address them, most recent works adopt convolutional neural networks (CNNs) for skin lesion analysis. However, due to the intrinsic locality of the convolution operator, CNNs lack the ability to capture contextual information and long-range dependency. To improve the baseline performance established by CNNs, we propose a Fully Transformer Network (FTN) to learn long-range contextual information for skin lesion analysis. FTN is a hierarchical Transformer computing features using Spatial Pyramid Transformer (SPT). SPT has linear computational complexity as it introduces a spatial pyramid pooling (SPP) module into multi-head attention (MHA)to largely reduce the computation and memory usage. We conduct extensive skin lesion analysis experiments to verify the effectiveness and efficiency of FTN using ISIC 2018 dataset. Our experimental results show that FTN consistently outperforms other state-of-the-art CNNs in terms of computational efficiency and the number of tunable parameters due to our efficient SPT and hierarchical network structure. The code and models will be public available at: https://github.com/Novestars/Fully-Transformer-Network.

PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers.

An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal MRIs from developing infant brains because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still during scanning. In addition, there are limited analytic approaches available. These challenges often lead to a significant reduction of usable MRI scans and pose a problem for modeling neurodevelopmental trajectories. Researchers have explored solving this problem by synthesizing realistic MRIs to replace corrupted ones. Among synthesis methods, the convolutional neural network-based (CNN-based) generative adversarial networks (GANs) have demonstrated promising performance. In this study, we introduced a novel 3D MRI synthesis framework- pyramid transformer network (PTNet3D)- which relies on attention mechanisms through transformer and performer layers. We conducted extensive experiments on high-resolution Developing Human Connectome Project (dHCP) and longitudinal Baby Connectome Project (BCP) datasets. Compared with CNN-based GANs, PTNet3D consistently shows superior synthesis accuracy and superior generalization on two independent, large-scale infant brain MRI datasets. Notably, we demonstrate that PTNet3D synthesized more realistic scans than CNN-based models when the input is from multi-age subjects. Potential applications of PTNet3D include synthesizing corrupted or missing images. By replacing corrupted scans with synthesized ones, we observed significant improvement in infant whole brain segmentation.

Improving across-dataset brain tissue segmentation for MRI imaging using transformer.

Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our method is inherently robust and can serve as a valuable tool for brain related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS.

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks.

Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.

ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates.

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.

Mapping Alveolar Oxygen Partial Pressure in COPD Using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.

Chronic obstructive pulmonary disease (COPD) and emphysema are characterized by functional and structural damage which increases the spaces for gaseous diffusion and impairs oxygen exchange. Here we explore the potential for hyperpolarized (HP) 3He MRI to characterize lung structure and function in a large-scale population-based study. Participants (n = 54) from the Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study, a nested case-control study of COPD among participants with 10+ packyears underwent HP 3He MRI measuring pAO2, apparent diffusion coefficient (ADC), and ventilation. HP MRI measures were compared to full-lung CT and pulmonary function testing. High ADC values (>0.4 cm2/s) correlated with emphysema and heterogeneity in pAO2 measurements. Strong correlations were found between the heterogeneity of global pAO2 as summarized by its standard deviation (SD) (p < 0.0002) and non-physiologic pAO2 values (p < 0.0001) with percent emphysema on CT. A regional study revealed a strong association between pAO2 SD and visual emphysema severity (p < 0.003) and an association with the paraseptal emphysema subtype (p < 0.04) after adjustment for demographics and smoking status. HP noble gas pAO2 heterogeneity and the fraction of non-physiological pAO2 results increase in mild to moderate COPD. Measurements of pAO2 are sensitive to regional emphysematous damage detected by CT and may be used to probe pulmonary emphysema subtypes. HP noble gas lung MRI provides non-invasive information about COPD severity and lung function without ionizing radiation.

Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study.

PURPOSE: To develop an end-to-end deep learning (DL) framework to segment ventilation defects on pulmonary hyperpolarized MRI. MATERIALS AND METHODS: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease (COPD) study is a nested longitudinal case-control study in older smokers. Between February 2016 and July 2017, 56 participants (age, mean ± SD, 74 ± 8 years; 34 men) underwent same breath-hold proton (1H) and helium (3He) MRI, which were annotated for non-ventilated, hypo-ventilated, and normal-ventilated lungs. In this retrospective DL study, 820 1H and 3He slices from 42/56 (75%) participants were randomly selected for training, with the remaining 14/56 (25%) for test. Full lung masks were segmented using a traditional U-Net on 1H MRI and were imported into a cascaded U-Net, which were used to segment ventilation defects on 3He MRI. Models were trained with conventional data augmentation (DA) and generative adversarial networks (GAN)-DA. RESULTS: Conventional-DA improved 1H and 3He MRI segmentation over the non-DA model (P = 0.007 to 0.03) but GAN-DA did not yield further improvement. The cascaded U-Net improved non-ventilated lung segmentation (P < 0.005). Dice similarity coefficients (DSC) between manually and DL-segmented full lung, non-ventilated, hypo-ventilated, and normal-ventilated regions were 0.965 ± 0.010, 0.840 ± 0.057, 0.715 ± 0.175, and 0.883 ± 0.060, respectively. We observed no statistically significant difference in DCSs between participants with and without COPD (P = 0.41, 0.06, and 0.18 for non-ventilated, hypo-ventilated, and normal-ventilated regions, respectively). CONCLUSION: The proposed cascaded U-Net framework generated fully-automated segmentation of ventilation defects on 3He MRI among older smokers with and without COPD that is consistent with our reference method.

Baby's best Foe-riend: Endogenous retroviruses and the evolution of eutherian reproduction.

High maternal investment in pregnancy and the perinatal period are prominent features of eutherian reproduction. Viviparity increases offspring survival, favoring high maternal prenatal investment. Matrotrophy through the placenta reduces maternal investment at early pregnancy, allowing the mother to abort embryos of subpar quality, therefore reducing resources wastage. On the other hand, intimate maternal-fetal interplay enables the fetus to manipulate maternal physiology to acquire more resources. This parent-offspring conflict likely drives the evolution of eutherian placentation, which is facilitated by the endogenous retroviruses (ERVs), ancient retroviruses that invaded host genome millions of years ago. ERVs bring new genes and novel regulatory elements into host genome, contribute to maternal-fetal tolerance, placenta-specific cell type formation, trophoblast gene expression network rewiring, and the establishment of imprinting. However, retroviruses/ERVs can function as infectious pathogens that interfere with host immune and inflammation pathways and cause genomic instability. In addition, ERVs coopted for host function may contribute to pathogenesis during infections due to their susceptibility to mechanisms activated by the invading pathogens. ERVs have been implicated in multiple perinatal adverse outcomes, therefore, eutherians must have evolved control mechanisms to regulate their function. Here we propose the TRIM family as an important participant of host antiviral defense and a likely candidate that mediates the coevolution of ERVs and their eutherian host. TRIMs have been shown to interact with retroviruses during each step of the infectious cycle. Understanding TRIMs' role in ERV regulation in the placenta may provide insight to both the physiology and pathology of eutherian reproduction.

Bioinformatics and Immune Infiltration Analyses Reveal the Key Pathway and Immune Cells in the Pathogenesis of Hypertrophic Cardiomyopathy.

Objective: This study was designed to identify the key pathway and immune cells for hypertrophic cardiomyopathy (HCM) via bioinformatics analyses of public datasets and evaluate the significance of immune infiltration in the pathogenesis of HCM. Methods: Expressional profiling from two public datasets (GSE36961 and GSE141910) of human HCM and healthy control cardiac tissues was obtained from the GEO database. After data preprocessing, differentially expressed genes (DEGs) were then screened between HCM and healthy control cardiac tissues in parallel. Gene Ontology, pathway functional enrichment, and gene set enrichment analysis were performed using DAVID and GSEA application. The compositional patterns of immune and stromal cells in HCM and control cardiac tissues were estimated based on the merged data using xCell. Protein-protein interaction (PPI) network and module analyses were constructed by STRING and Cytoscape applications. Gender-based expressional differences analyses were also conducted to explore gender differences in HCM. GSE130036 and clinical samples were used for verification analyses. Results: A total of 310 DEGs were identified. Upregulated DEGs were mainly enriched in "adhesion" and "apoptotic process" in the biological process. As for the downregulated DEGs, "inflammatory response," "innate immune response," "phagosome," and "JAK-STAT signaling pathway" were highly enriched. Immune infiltration analyses suggested that the scores of macrophages, monocytes, DC, Th1, Treg, and plasma cells in the HCM group were significantly decreased, while CD8+ T cells, basophils, fibroblasts, and platelets were significantly enriched. Module analyses revealed that STAT3, as the hub genes in HCM together with LYVE1+CD163+ macrophages, may play a key role in the pathogenesis of HCM while there were no obvious gender differences in the HCM samples from selected datasets. Verification analyses performed on GSE130036 and clinical samples showed a strong positive correlation (Spearman correlation = 0.7646) and a good co-localization relationship between LYVE1 and CD163, suggesting the potential function of LYVE1+CD163+ macrophages in maintaining the homeostasis of cardiac tissue. Conclusion: STAT3-related pathway and CD163+LYVE1+ macrophages were identified as the potential key pathway and immune cells in HCM and may serve as interesting targets for further in-depth research.

Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers.

Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.