Familial and genetic association with neurodevelopmental disorders caused by a heterozygous variant in the SRRM2 gene.

Background: Neurodevelopmental disorders (NDDs) are a class of disorders affecting brain development and function, characterized by an inability to reach cognitive, emotional, and motor developmental milestones. The pathology of NDDs is complex. A recent study found that variants in the SRRM2 gene cause NDDs. However, genetic conditions play the most important role in the etiology of NDD. The genetic causes of NDD are extremely heterogeneous, leading to certain challenges in clinical diagnosis. Methods: A pregnant woman with congenital intelligence disorder came to our hospital for genetic diagnosis to predict the status of her fetus. Her mother and a brother also suffer from congenital intelligence disorder. She has a daughter with speech delay. Whole exome sequencing was used to identify a mutation (c.1415C>G) in the SRRM2 gene of this family that resulted in a change in the 472nd amino acid residue of the SRRM2 protein from serine to terminated. Conclusion: We report a family with an autosomal dominant genetic disorder caused by variants in the SRRM2 gene causing NDDs. Prenatal diagnosis can help patients with this genetic disorder to have healthy offspring.

Pose-Driven Realistic 2-D Motion Synthesis.

A realistic 2-D motion can be treated as a deforming process of an individual appearance texture driven by a sequence of human poses. In this article, we thereby propose to transform the 2-D motion synthesis into a pose conditioned realistic motion image generation task considering the promising performance of pose estimation technology and generative adversarial nets (GANs). However, the problem is that GAN is only suitable to do the region-aligned image translation task while motion synthesis involves a large number of spatial deformations. To avoid this drawback, we design a two-step and multistream network architecture. First, we train a special GAN to generate the body segment images with given poses in step-I. Then in step-II, we input the body segment images as well as the poses into the multistream network so that it only needs to generate the textures in each aligned body region. Besides, we provide a real face as another input of the network to improve the face details of the generated motion image. The synthesized results with realism and sharp details on four training sets demonstrate the effectiveness of the proposed model.

A Deep Learning Framework for Start-End Frame Pair-Driven Motion Synthesis.

A start-end frame pair and a motion pattern-based motion synthesis scheme can provide more control to the synthesis process and produce content-various motion sequences. However, the data preparation for the motion training is intractable, and concatenating feature spaces of the start-end frame pair and the motion pattern lacks theoretical rationality in previous works. In this article, we propose a deep learning framework that completes automatic data preparation and learns the nonlinear mapping from start-end frame pairs to motion patterns. The proposed model consists of three modules: action detection, motion extraction, and motion synthesis networks. The action detection network extends the deep subspace learning framework to a supervised version, i.e., uses the local self-expression (LSE) of the motion data to supervise feature learning and complement the classification error. A long short-term memory (LSTM)-based network is used to efficiently extract the motion patterns to address the speed deficiency reflected in the previous optimization-based method. A motion synthesis network consists of a group of LSTM-based blocks, where each of them is to learn the nonlinear relation between the start-end frame pairs and the motion patterns of a certain joint. The superior performances in action detection accuracy, motion pattern extraction efficiency, and motion synthesis quality show the effectiveness of each module in the proposed framework.

Local Self-Expression Subspace Learning Network for Motion Capture Data.

Deep subspace learning is an important branch of self-supervised learning and has been a hot research topic in recent years, but current methods do not fully consider the individualities of temporal data and related tasks. In this paper, by transforming the individualities of motion capture data and segmentation task as the supervision, we propose the local self-expression subspace learning network. Specifically, considering the temporality of motion data, we use the temporal convolution module to extract temporal features. To implement the local validity of self-expression in temporal tasks, we design the local self-expression layer which only maintains the representation relations with temporally adjacent motion frames. To simulate the interpolatability of motion data in the feature space, we impose a group sparseness constraint on the local self-expression layer to impel the representations only using selected keyframes. Besides, based on the subspace assumption, we propose the subspace projection loss, which is induced from distances of each frame projected to the fitted subspaces, to penalize the potential clustering errors. The superior performances of the proposed model on the segmentation task of synthetic data and three tasks of real motion capture data demonstrate the feature learning ability of our model.

A novel isoform of hydroxyacyl-CoA dehydrogenase inhibits cell proliferation.

Hydroxyacyl-CoA dehydrogenase (HADH) catalyzes the third reaction of mitochondrial ß-oxidation cascade, while the regulation of its expression and function remains to be elucidated. Using the quantitative translation initiation sequencing (QTI-seq), we have identified that murine Hadh mRNA has two alternative translation start codons. We demonstrated that translation from upstream start codon encodes the mitochondrial isoform of HADH, while translation from downstream start codon produces a short isoform (HADH-S) with predominant nuclear localization. Moreover, overexpression of HADH-S inhibits the proliferation of mouse embryonic fibroblasts. Overall, our results identify a novel isoform of HADH participating in cell proliferation.

Profiling of exosomal microRNAs expression in umbilical cord blood from normal and preeclampsia patients.

BACKGROUND: Epidemiological and experimental studies suggest that preeclampsia has a negative impact on maternity and offspring health. Previous studies report that dysregulation in utero-environment increases risk for elderly disease such as cardiovascular disease. However, the underlying mechanisms remain elusive. Specific microRNAs (miRNAs) are packaged in exosomes may regulate microvascular dysfunction in offspring of mothers with preeclampsia. The present study aimed to identify the differential expression profiles of microRNAs in the serum exosomes between patients with preeclampsia and normal pregnancies. METHODS: A comprehensive miRNA sequence-based approach was performed to compare exosomes carry miRNAs (Exo-miRNAs) expression levels in umbilical serum between normal and preeclampsia patients. Exosomes were isolated using the ExoQuick precipitation kit. Serum exosomes were then viewed under electron microscopy, and their characteristics determined by western blotting and nanoparticle-tracking analysis. Illumina platform was used to perform sequencing. Bioinformatics analysis was used to explore differentially expressed Exo-miRNAs in umbilical serum. RESULTS: Based on sequence similarity, 1733 known miRNAs were retrieved. Furthermore, 157 mature miRNAs in serum exosomes were significantly differential expressed between PE and those control groups (P<0.05, log2|FC| > 1). Out, of the 157 miRNAs, 96 were upregulated miRNAs whereas 61 miRNAs were downregulated. The 157 differentially expressed miRNAs targeted 51,424 differentially expressed genes. Functional analysis through KEGG pathway and Gene Ontology results uncovered that target genes of miRNAs with differential expression were significantly linked to several pathways and biological processes. CONCLUSION: The findings of this study showed differential expression of umbilical serum Exo-miRNAs in normal compared with PE patients, implying that these Exo-miRNAs may associate with microvascular dysfunction in offspring of mothers with preeclampsia.

iTRAQ-based quantitative proteomic analysis of thoracic aortas from adult rats born to preeclamptic dams.

BACKGROUND: Preeclampsia and gestational hypertension can cause vascular function impairment in offspring. In our previous work, we described the protein expression profiles of umbilical artery tissues from patients with preeclampsia. METHODS: To gain insights into the mechanisms of vascular dysfunction in adult rats born to preeclamptic dams, we analyzed thoracic aorta tissues by using iTRAQ isobaric tags and 2D nano LC-MS/MS. RESULTS: By using the iTRAQ method, we analyzed 1825 proteins, of which 106 showed significantly different expression in the thoracic aortic. Ingenuity pathway analysis (IPA) showed that the majority of differentially expressed proteins (DEPs) were associated with cardiovascular function. Further analysis indicated that glucose-6-phosphate dehydrogenase (G6PD), which is inhibited by miR-423-5p and activated by TP53, had the strongest effect on cardiovascular function. The expression of G6PD was upregulated in thoracic aorta tissues, as confirmed by Western blotting. The expression of two other vascular function-related proteins, cysteine- and glycine-rich protein 2 (CSRP2) and tubulin alpha-4 A (TUBA4A), was upregulated, as demonstrated by mass spectrometry (MS). CONCLUSIONS: Although the results require further functional validation, these data provide novel findings related to vascular function impairment in the adult offspring of preeclamptic mothers.

Nonconvex Low-Rank Kernel Sparse Subspace Learning for Keyframe Extraction and Motion Segmentation.

By exploiting the kernel trick, the sparse subspace model is extended to the nonlinear version with one or a combination of predefined kernels, but the high-dimensional space induced by predefined kernels is not guaranteed to be able to capture the features of the nonlinear data in theory. In this article, we propose a nonconvex low-rank learning framework in an unsupervised way to learn a kernel to replace the predefined kernel in the sparse subspace model. The learned kernel by a nonconvex relaxation of rank can better exploiting the low-rank property of nonlinear data to induce a high-dimensional Hilbert space that more closely approaches the true feature space. Furthermore, we give a global closed-form optimal solution of the nonconvex rank minimization and prove it. Considering the low-rank and sparseness characteristics of motion capture data in its feature space, we use them to verify the better representation of nonlinear data with the learned kernel via two tasks: keyframe extraction and motion segmentation. The performances on both tasks demonstrate the advantage of our model over the sparse subspace model with predefined kernels and some other related state-of-art methods.

Nonlinear Low-Rank Matrix Completion for Human Motion Recovery.

Human motion capture data has been widely used in many areas, but it involves a complex capture process and the captured data inevitably contains missing data due to the occlusions caused by the actor's body or clothing. Motion recovery, which aims to recover the underlying complete motion sequence from its degraded observation, still remains as a challenging task due to the nonlinear structure and kinematics property embedded in motion data. Low-rank matrix completion based methods have shown promising performance in short-time-missing motion recovery problems. However, low-rank matrix completion, which is designed for linear data, lacks the theoretic guarantee when applied to the recovery of nonlinear motion data. To overcome this drawback, we propose a tailored nonlinear matrix completion model for human motion recovery. Within the model, we first learn a combined low-rank kernel via multiple kernel learning. By exploiting the learned kernel, we embed the motion data into a high dimensional Hilbert space where motion data is of desirable low-rank and we then use the low-rank matrix completion to recover motions. In addition, we add two kinematic constraints to the proposed model to preserve the kinematics property of human motion. Extensive experiment results and comparisons with five other state-of-the-art methods demonstrate the advantage of the proposed method.

Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering.

Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.

Hypoxia-inducible microRNA-218 inhibits trophoblast invasion by targeting LASP1: Implications for preeclampsia development.

Preeclampsia (PE) is a major contributor to maternal morbidity and mortality. However, the molecular mechanisms underlying PE progression are not well characterized. Here, we investigated the role of miR-218 in PE development. The expression of miR-218 and its host genes SLIT2 and SLIT3 was up-regulated in preeclamptic placentae compared to normal placentae. miR-218 expression was induced by hypoxia and decreased after knockdown of HIF-1α in an extravillous trophoblast cell line (HTR-8/SVneo). Chromatin immunoprecipitation assays showed direct binding of HIF-1α to the promoters of SLIT2 and SLIT3. Bioinformatics analysis identified LASP1 as a direct target of miR-218. Overexpression of miR-218 repressed the expression of LASP1 at both the mRNA and protein level. Meanwhile, miR-218 repressed the activity of a luciferase reporter containing the 3'-untranslated region of the LASP1 gene. Furthermore, expression of LASP1 rescued the inhibitory effect of miR-218 on HTR-8/SVneo cell invasion. Together, these results indicated that miR-218 contributes to PE by targeting LASP1 to inhibit trophoblast invasion.

MicroRNA-218 targets adiponectin receptor 2 to regulate adiponectin signaling.

Adiponectin exerts an antidiabetic function through the adiponectin receptors 1 and 2 (AdipoR1 and AdipoR2). The mechanism regulating the expression of adiponectin receptors remains to be elucidated. Bioinformatics analysis demonstrated that microRNA (miR)­218 targets the 3' untranslated region (3'UTR) of the AdipoR2 mRNA. The present study aimed to investigate whether miR-218 regulated the expression of AdipoR2 using immunoblotting, reverse transcription quantitative polymerase chain reaction and luciferase assays. The protein level and the mRNA level of AdipoR2 were reduced when miR­218 was expressed in HepG2 cells. Additionally, overexpression of miR­218 repressed the activity of a luciferase reporter containing the 3'UTR of AdipoR2. Furthermore, the present study aimed to determine whether miR-218 regulated glucose metabolism through detecting signaling pathways and glucose uptake. The phosphorylation of AMP­activated protein kinase and p38 mitogen­activated protein kinase was reduced in miR­218­expressing cells. In addition, miR­218 inhibited adiponectin­induced glucose uptake. The present results suggested that miR­218 targets AdipoR2 to inhibit adiponectin signaling.

Expression of PI3-K, PKB and GSK-3 ß in the skeletal muscle tissue of gestational diabetes mellitus.

OBJECTIVE: To analyze the expression of phosphatidylinositol 3 kinase (PI3-K), protein kinase B (PKB) and glycogen synthase kinase 3 beta (GSK-3 ß) in skeletal muscle tissue of gestational diabetes mellitus (GDM). METHODS: A total of 90 cases of pregnant women were divided into observation group and control group according to the occurrence of GDM with 45 cases in either, and the expression of PI3-K, PKB, GSK-3 ß mRNA expression in skeletal muscle tissue was compared between two groups. RESULTS: The total PI3-K p85 protein was significantly higher in the observation group compared with the control group, the activity of PI3-K was lower than that of the latter; The total PKB, GSK-3 ß protein in skeletal tissue had no significant difference between two groups, while the serine phosphorylation levels of PKB and GSK-3ß were significantly lower in observation group compared with the control group. CONCLUSIONS: The downregulation of PI3-K, PKB and GSK-3ßin skeletal tissue of GDM caused by phosphorylation dysfunction of signaling molecules is the reason for insulin resistance and transporter function decline which lead to GDM.

Alterations of angiopoietin-related growth factor (Angptl6) during pregnancy and in pre-eclampsia.

AIM: The aim of this study was to clarify the alterations of angiopoietin-related growth factor (AGF, also known as Angptl6/ARP5) in the serum of normally pregnant women and pre-eclamptic women and to explore the role of AGF in pre-eclampsia. MATERIAL AND METHODS: Thirty healthy non-pregnant women, 118 normally pregnant women (30 at 6-12 gestational weeks, 30 at 13-28 weeks and 58 at 29-40 weeks, respectively) and 46 pre-eclamptic women were recruited. Fasting blood samples were obtained from all subjects. Fifteen women of healthy pregnant women provided their blood samples at 24 and 48 h after cesarean section, respectively. Enzyme-linked immunosorbent assay was used to determine the serum level of AGF. RESULTS: Serum levels of AGF were significantly higher in normally pregnant women than non-pregnant women (P < 0.001 of all). Although serum AGF of the first trimester was significantly higher than that of the second trimester (P = 0.033), there were no significant differences of serum AGF in the comparison between the first and third trimester (P = 0.064) and between the second and third trimester (P = 0.546). Serum AGF significantly decreased after delivery (P < 0.05). There were no significant differences in AGF of maternal serum between pre-eclamptic women and normally pregnant control women (P = 0.285). There were no significant differences in circulating AGF concentration between non-pregnant women and postpartum groups (P = 0.052 for 24 h after delivery and P = 0.083 for 48 h after delivery). CONCLUSION: The serum level of AGF was elevated in normal pregnancy compared with non-pregnant women, suggesting that placenta is an important source of circulating AGF during pregnancy.

Alterations of maternal serum and placental follistatin-like 3 and myostatin in pre-eclampsia.

AIM: To clarify the alterations of myostatin, a member of the transforming growth factor-ß superfamily, and follistatin-like 3 (FSTL3), a binding protein for myostatin, in pre-eclamptic women. METHODS: Samples of blood and placenta were collected from 40 pre-eclamptic women and 40 controls. The serum level and placental expression of FSTL3 and myostatin were determined with enzyme-linked immunosorbent assay, real-time polymerized chain reaction and western blotting. RESULTS: The serum levels of myostatin and FSTL3 were significantly higher in pre-eclamptic women than in the controls (P < 0.001 for both). Placental expression of myostatin and FSTL3 were also significantly increased in the pre-eclamptic placenta compared with that of the controls (P < 0.001 for both); however, there were no significant differences in myostatin or FSTL3 in either the maternal serum or the placenta in women with mild or severe pre-eclampsia (P > 0.05 for both). CONCLUSION: The serum levels and placental expression of myostatin and FSTL3 are elevated in pre-eclampsia, suggesting the role of myostatin and its binding protein in pre-eclampsia.

Peripartum serum leptin and soluble leptin receptor levels in women with gestational diabetes.

The purpose of the study was to clarify the alterations in serum leptin and soluble leptin receptor levels at term and early postpartum in gestational diabetes mellitus (GDM). Twenty women with normal pregnancy and 20 with GDM were recruited and blood samples were taken on the day of delivery and Days 1, 3 and 5 after delivery. Serum leptin levels were significantly higher in women with GDM than in the controls before delivery and decreased significantly after delivery (p < 0.001). After delivery there were no significant differences in serum leptin concentrations between women with GDM and the controls. Serum soluble leptin receptor concentrations did not differ neither between the two groups, nor before or after delivery. Leptin may play a role in GDM through a positive correlation with insulin resistance.