DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

Neuron type-specific proteomics reveals distinct Shank3 proteoforms in iSPNs and dSPNs lead to striatal synaptopathy in Shank3B-/- mice.

Combinatorial expression of postsynaptic proteins underlies synapse diversity within and between neuron types. Thus, characterization of neuron-type-specific postsynaptic proteomes is key to obtaining a deeper understanding of discrete synaptic properties and how selective dysfunction manifests in synaptopathies. To overcome the limitations associated with bulk measures of synaptic protein abundance, we developed a biotin proximity protein tagging probe to characterize neuron-type-specific postsynaptic proteomes in vivo. We found Shank3 protein isoforms are differentially expressed by direct and indirect pathway spiny projection neurons (dSPNs and iSPNs). Investigation of Shank3B-/- mice lacking exons 13-16 within the Shank3 gene, reveal distinct Shank3 protein isoform expression in iSPNs and dSPNs. In Shank3B-/- striatum, Shank3E and Shank3NT are expressed by dSPNs but are undetectable in iSPNs. Proteomic analysis indicates significant and selective alterations in the postsynaptic proteome of Shank3B-/- iSPNs. Correspondingly, the deletion of exons 13-16 diminishes dendritic spine density, reduces spine head diameter, and hampers corticostriatal synaptic transmission in iSPNs. Remarkably, reintroducing Shank3E in adult Shank3B-/- iSPNs significantly rectifies the observed dendritic spine morphological and corticostriatal synaptic transmission deficits. We report unexpected cell-type specific synaptic protein isoform expression which could play a key causal role in specifying synapse diversity and selective synapse dysfunction in synaptopathies.

A Pathogenic Missense Mutation in Kainate Receptors Elevates Dendritic Excitability and Synaptic Integration through Dysregulation of SK Channels.

Numerous rare variants that cause neurodevelopmental disorders (NDDs) occur within genes encoding synaptic proteins, including ionotropic glutamate receptors. However, in many cases, it remains unclear how damaging missense variants affect brain function. We determined the physiological consequences of an NDD causing missense mutation in the GRIK2 kainate receptor (KAR) gene, that results in a single amino acid change p.Ala657Thr in the GluK2 receptor subunit. We engineered this mutation in the mouse Grik2 gene, yielding a GluK2(A657T) mouse, and studied mice of both sexes to determine how hippocampal neuronal function is disrupted. Synaptic KAR currents in hippocampal CA3 pyramidal neurons from heterozygous A657T mice exhibited slow decay kinetics, consistent with incorporation of the mutant subunit into functional receptors. Unexpectedly, CA3 neurons demonstrated elevated action potential spiking because of downregulation of the small-conductance Ca2+ activated K+ channel (SK), which mediates the post-spike afterhyperpolarization. The reduction in SK activity resulted in increased CA3 dendritic excitability, increased EPSP-spike coupling, and lowered the threshold for the induction of LTP of the associational-commissural synapses in CA3 neurons. Pharmacological inhibition of SK channels in WT mice increased dendritic excitability and EPSP-spike coupling, mimicking the phenotype in A657T mice and suggesting a causative role for attenuated SK activity in aberrant excitability observed in the mutant mice. These findings demonstrate that a disease-associated missense mutation in GRIK2 leads to altered signaling through neuronal KARs, pleiotropic effects on neuronal and dendritic excitability, and implicate these processes in neuropathology in patients with genetic NDDs.SIGNIFICANCE STATEMENT Damaging mutations in genes encoding synaptic proteins have been identified in various neurodevelopmental disorders, but the functional consequences at the cellular and circuit level remain elusive. By generating a novel knock-in mutant mouse, this study examined the role of a pathogenic mutation in the GluK2 kainate receptor (KAR) subunit, a subclass of ionotropic glutamate receptors. Analyses of hippocampal CA3 pyramidal neurons determined elevated action potential firing because of an increase in dendritic excitability. Increased dendritic excitability was attributable to reduced activity of a Ca2+ activated K+ channel. These results indicate that a pathogenic KAR mutation results in dysregulation of dendritic K+ channels, which leads to an increase in synaptic integration and backpropagation of action potentials into distal dendrites.

Mixtures of strategies underlie rodent behavior during reversal learning.

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

Outcomes of vaginal repair and vaginal repair combined with GnRHa administration in the treatment of cesarean section scar defects: A randomized clinical trial.

STUDY OBJECTIVE: To prospectively investigate whether the application of vaginal repair (VR) of cesarean section scar defect (CSD) combined with a gonadotropin-releasing hormone agonist (GnRHa) achieve better clinical outcomes than VR alone. DESIGN: A randomized clinical trial. SETTING: University hospital. PATIENTS: A total of 124 women with CSD were undergoing expectant management from December 2016 to September 2021. 61 were randomised to VR+ GnRHa and 63 to VR alone. INTERVENTION: Vaginal repair combined with GnRHa and vaginal repair alone. MEASURES AND MAIN RESULTS: The primary outcome was the duration of menstruation and thickness of the remaining muscular layer (TRM) at 6 months after surgery. Secondary outcomes included the length, width and depth of the CSD; operation time; estimated blood loss; hospitalization time; and operative complications. Women were treated with either VR (n = 63) or VR + GnRHa (n = 61). Menstruation and TRM in patients pre. vs. post comparisons either with VR or VR + GnRHa are significant improved (P < .05). Significant differences in menstruation duration and TRM occurred in patients treated with VR + GnRHa compared with those treated with VR (P < .05). Moreover, the rate of CSD after surgery in the VR group was significantly higher than that in the VR + GnRHa group (P = .033), and CSD patients in the VR + GnRHa group achieved better therapeutic effects than those in the VR group (P = .017). Patients who received VR + GnRHa had a shorter menstruation duration and a greater increment of TRM postoperatively than did patients treated with VR alone (P = .021; P = .002, respectively). CONCLUSION: VR + GnRHa therapy has a greater potential to improve scar healing and reduce the number of menstruation days than VR alone for symptomatic women with CSD. PRéCIS: Vaginal Repair Combined with GnRHa Creates Better Therapeutic Effects of CSD. TRIAL REGISTRATION: Date of registration: October 13, 2016, Date of initial participant enrollment: December 20, 2016, Clinical trial identification number: NCT02932761, URL of the registration site: ClinicalTrials.gov, Figshare DOI: 10.6084/m9.figshare.24117114 LINK TO THE CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT02932761.

A Prediction Model for the Efficacy of Transvaginal Repair in Patients With Cesarean Scar Defect: An Evidence-Based Proposal for Patient Selection.

STUDY OBJECTIVE: To establish a prediction model to help doctors determine which patients with cesarean scar defect are more suitable for transvaginal repair. DESIGN: Retrospective analysis. SETTING: Xinhua Hospital and Shanghai First Maternity & Infant Hospital between June 2014 and May 2021. PATIENTS: 1015 women who underwent transvaginal repair of cesarean scar defect (CSD). INTERVENTIONS: All enrolled patients underwent CSD repair performed by the same gynecologist and his team. And followed up a clinic visit at 6 months to record their menstruation and measure multiple parameters of the CSD by Magnetic Resonance Imaging. MAIN OUTCOMES AND MEASURES: CSD patients are categorized as optimal healing group when the menstruation duration is no more than 7 days, meanwhile the thickness of residual myometrium is no less than 5.39 mm after vaginal repair. The final nomogram is constructed to predict surgical outcomes based on preoperative variables. RESULTS: The key factors that determine optimal healing are the timing of cesarean section (elective or emergency), menstrual cycle, CSD length, width, depth, and the thickness of the lower uterine segment. With the prediction model, scores are given to each parameter according to the statistics. Total scores range from 0 to 25 points, with a cutoff point of 16.5. When a score is greater than 16.5, the transvaginal repair can achieve optimal healing. Uterine position (anteflexion or retroflexion) and preoperative thickness of residual myometrium are the key factors affecting postoperative thickness of residual myometrium. The width of the CSD and the thickness of the lower uterine segment are the key factors affecting abnormal uterine bleeding symptoms (p < 0.01). CONCLUSIONS: For the first time, we established a prediction model system that may predict the repair effect of CSD and can potentially be useful in future clinical trials to determine which patients are more suitable for surgery or other treatment options.

Single-center phase 2 study of PD-1 inhibitor combined with DNA hypomethylation agent + CAG regimen in patients with relapsed/refractory acute myeloid leukemia.

Anti-PD-1 monotherapy had limited clinical efficacy in relapsed/refractory (r/r) AML patients with higher PD-1 and PD-L1 expression. Hence, we investigated the efficacy and safety of PD-1 inhibitor with DNA hypomethylating agent (HMA) + CAG regimen in patients who had failed prior AML therapy. In this phase 2, single-arm study, r/r AML patients received azacitidine or decitabine plus CAG regimen with tislelizumab. Primary endpoints were efficacy (objective response rate [ORR]) and safety. Secondary endpoints included overall survival (OS), event-free survival (EFS) and duration of response (DOR). Statistical analyses were performed using Stata 14.0 and SPSS 20.0 software where P < 0.05 denoted significance. Twenty-seven patients were enrolled patients and completed 1 cycle, and 14 (51.9%) and 4 (14.8%) patients completed 2 and 3 cycles, respectively. ORR was 63% (14: complete remission [CR]/CR with incomplete hematologic recovery [CRi], 3: partial remission (PR), 10: no response [NR]). Median OS (mOS) and EFS were 9.7 and 9.2 months, respectively. With a median follow-up of 8.2 months (1.1-26.9), the mOS was not reached in responders (CR/CRi/PR) while it was 2.4 months (0.0-5.4) in nonresponders (P = 0.002). Grade 2-3 immune-related adverse events (irAEs) were observed in 4 (14.8%) patients and 3 nonresponders died of lung infection after treatment. Tislelizumab + HMA + CAG regimen showed improved outcomes in r/r AML patients with lower pretherapy leukemia burden. irAEs were mild and low-grade and higher pretherapy bone marrow CD4+ CD127+ PD-1+ T cells might serve as a predictor of treatment response.ClinicalTrials.gov identifier NCT04541277.

Targeted delivery of a PD-1-blocking scFv by CD133-specific CAR-T cells using nonviral Sleeping Beauty transposition shows enhanced antitumour efficacy for advanced hepatocellular carcinoma.

BACKGROUND: CD133 is considered a marker for cancer stem cells (CSCs) in several types of tumours, including hepatocellular carcinoma (HCC). Chimeric antigen receptor-specific T (CAR-T) cells targeting CD133-positive CSCs have emerged as a tool for the clinical treatment of HCC, but immunogenicity, the high cost of clinical-grade recombinant viral vectors and potential insertional mutagenesis limit their clinical application. METHODS: CD133-specific CAR-T cells secreting PD-1 blocking scFv (CD133 CAR-T and PD-1 s cells) were constructed using a sleeping beauty transposon system from minicircle technology, and the antitumour efficacy of CD133 CAR-T and PD-1 s cells was analysed in vitro and in vivo. RESULTS: A univariate analysis showed that CD133 expression in male patients at the late stage (II and III) was significantly associated with worse progression-free survival (PFS) (P = 0.0057) and overall survival (OS) (P = 0.015), and a multivariate analysis showed a trend toward worse OS (P = 0.041). Male patients with advanced HCC exhibited an approximately 20-fold higher PD-L1 combined positive score (CPS) compared with those with HCC at an early stage. We successfully generated CD133 CAR-T and PD-1 s cells that could secrete PD-1 blocking scFv based on a sleeping beauty system involving minicircle vectors. CD133 CAR-T and PD-1 s cells exhibited significant antitumour activity against HCC in vitro and in xenograft mouse models. Thus, CD133 CAR-T and PD-1 s cells may be a therapeutically tractable strategy for targeting CD133-positive CSCs in male patients with advanced HCC. CONCLUSIONS: Our study provides a nonviral strategy for constructing CAR-T cells that could also secrete checkpoint blockade inhibitors based on a Sleeping Beauty system from minicircle vectors and revealed a potential benefit of this strategy for male patients with advanced HCC and high CD133 expression (median immunohistochemistry score > 2.284).

Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma.

Costimulatory molecules are an indispensable signal for activating immune cells. However, the features of many costimulatory molecule genes (CMGs) in lung adenocarcinoma (LUAD) are poorly understood. This study systematically explored expression patterns of CMGs in the tumor immune microenvironment (TIME) status of patients with LUAD. Their expression profiles were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Two robust TIME subtypes ("hot" and "cold") were classified by K-means clustering and estimation of stromal and immune cells in malignant tumor tissues using expression data. The "hot" subtype presented higher infiltration in activated immune cells and enrichments in the immune cell receptor signaling pathway and adaptive immune response. Three CMGs (CD80, LTB, and TNFSF8) were screened as final diagnostic markers by means of Least Absolute Shrinkage Selection Operator and Support Vector Machine-Recursive Feature Elimination algorithms. Accordingly, the diagnostic nomogram for predicting individualized TIME status showed satisfactory diagnostic accuracy in The Cancer Genome Atlas training cohort as well as GSE31210 and GSE180347 validation cohorts. Immunohistochemistry staining of 16 specimens revealed an apparently positive correlation between the expression of CMG biomarkers and pathologic response to immunotherapy. Thus, this diagnostic nomogram provided individualized predictions in TIME status of LUAD patients with good predictive accuracy, which could serve as a potential tool for identifying ideal candidates for immunotherapy.

Experimental quantum natural gradient optimization in photonics.

Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the noisy intermediate-scale quantum era. The performance of VQAs heavily depends on the optimization method. Compared with gradient-free and ordinary gradient descent methods, the quantum natural gradient (QNG), which mirrors the geometric structure of the parameter space, can achieve faster convergence and avoid local minima more easily, thereby reducing the cost of circuit executions. We utilized a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time, to the best of our knowledge. We obtained the dissociation curve of the He-H+ cation and achieved chemical accuracy, verifying the outperformance of QNG optimization on a photonic device. Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.

Quantum generative adversarial learning in photonics.

Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.

Dissecting climate change risk and financial market instability: Implications for ecological risk management.

This research investigates the impact of climate challenges on financial markets by introducing an innovative approach to measure climate risk, specifically the aggregate climate change concern (ACCC) index. The study aims to assess and quantify the potential influence of climate change and risk-related factors on the performance and dynamics of financial markets. In this paper, concern is defined as the attention paid to the risk of climate change and the associated negative consequences. The findings demonstrate that the aggregate index exhibits robust predictability of market risk premiums, both within the sample and out-of-sample. By comparison, the index contains additional information beyond 14 economic predictors and 12 risk/uncertainty indexes in forecasting stock market return. In addition, the index proves valuable for mean-variance investors in asset allocation, leading to significant economic gains. The study identifies the index's ability to capture the reversal of temporary price crashes caused by overreactions to climate change risk. Furthermore, it exhibits stronger return forecasting capability for green stocks, non-state-owned enterprise (non-SOE) stocks, and stocks in regions with low air pollution. Particularly during periods of low air pollution and relaxed regulation, the index displays an enhanced ability to forecast returns. The study's findings provide valuable insights for policymakers and financial institutions as they address 21st-century environmental challenges. Moreover, these findings can inform the design of adaptive measures and interventions aimed at mitigating ecological risks and promoting sustainable economic growth.

Pankun: A New Generation of Broadband Ocean Bottom Seismograph.

This paper presents a new broadband ocean bottom seismograph (OBS) developed by the SUSTech OBS lab for passive-source seafloor seismic observations. This instrument, called Pankun, has several key features that set it apart from traditional OBS instruments. In addition to the seismometer-separated scheme, these features include a unique shielding structure to minimize current-induced noise, a compact gimbal for accurate leveling, and low power consumption for extended operation on the seafloor. The design and testing of Pankun's primary components are thoroughly described in this paper. The instrument has been successfully tested in the South China Sea, demonstrating its ability to record high-quality seismic data. The anti-current shielding structure of Pankun OBS has the potential to improve low-frequency signals, particularly on the horizontal components, in seafloor seismic data.

Variational Quantum Process Tomography of Non-Unitaries.

Quantum process tomography is a fundamental and critical benchmarking and certification tool that is capable of fully characterizing an unknown quantum process. Standard quantum process tomography suffers from an exponentially scaling number of measurements and complicated data post-processing due to the curse of dimensionality. On the other hand, non-unitary operators are more realistic cases. In this work, we put forward a variational quantum process tomography method based on the supervised quantum machine learning framework. It approximates the unknown non-unitary quantum process utilizing a relatively shallow depth parametric quantum circuit and fewer input states. Numerically, we verified our method by reconstructing the non-unitary quantum mappings up to eight qubits in two cases: the weighted sum of the randomly generated quantum circuits and the imaginary time evolution of the Heisenberg XXZ spin chain Hamiltonian. Results show that those quantum processes could be reconstructed with high fidelities (>99%) and shallow depth parametric quantum circuits (d≤8), while the number of input states required is at least two orders of magnitude less than the demands of the standard quantum process tomography. Our work shows the potential of the variational quantum process tomography method in characterizing non-unitary operators.

Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits.

Quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4^{n} to 2^{n-m}), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.

Vaginal repair of cesarean section scar defects: Preoperative hysteroscopic evaluation.

INTRODUCTION: Cesarean section scar defects (CSDs) are one of the long-term complications following cesarean section. They can be detected by transvaginal sonography, hysterosalpingography, sonohysterography and magnetic resonance imaging (MRI). Hysteroscopy is frequently used in evaluating endometrial disease. However, the description of CSDs by hysteroscopy is very limited. Only a few papers about hysteroscopy evaluation have been published. This is an exploratory study to compare hysteroscopic findings with myometrial thickness and post-surgical outcomes. MATERIAL AND METHODS: From February 2019 to December 2020, 143 women with CSDs were enrolled in the observational study. All women suffered from abnormal uterine bleeding and were evaluated in a standardized way with hysteroscopy before vaginal surgery. Dome-shaped CSDs could be clearly observed in all patients under hysteroscopy. We recorded the pictures of each patient under hysteroscopy and classified them. All patients underwent outpatient review at 3 and 6 months after surgery to obtain menstrual information and CSD scar size by MRI or transvaginal sonography. RESULTS: Pale mucosae in the defect were meager endometrial lining covering the surface of muscle layer, cyst lesions were some cyst lesions in the defect, increased local vascularization was a vascular tree with branching and irregular vascular distribution in defect, polypoid lesions were polypoid lesions in the defect, and serrated niches were two niches at the anterior uterine isthmus. The features of the CSDs observed under hysteroscopy were identified as five phenotypes: pale mucosae (90/143, 62.9%), cyst lesions (23/143, 16.1%), polypoid lesions (19/143, 13.3%), increased local vascularization (27/143, 18.9%) and serrated niches (7/143, 4.9%). The most common finding in scar defects under hysteroscopy was pale mucosae in the CSD. The results suggest that patients with increased local vascularization and serrated niches have a high risk of thinner residual myometrium before vaginal repair (p < 0.05). However, there was no significant difference in menstrual duration or in the outcome of vaginal repair for CSDs between these five phenotypes (p > 0.05). CONCLUSIONS: Patients with the abnormal blood vessel or serration phenotypes of defects under hysteroscopy may have a thinner residual myometrium. The phenotypes of hysteroscopic findings of CSDs have no correlation with the outcome of repair.

Margin purchases, short sales and stock return volatility in China: Evidence from the COVID-19 outbreak.

In this paper, we investigate the effects of margin purchases and short sales on the return volatility in the Chinese stock market during the COVID-19 outbreak. We present two main findings. First, we show that stocks with higher level of margin-trading activity exhibit higher return volatility. The COVID-19 outbreak amplifies the destabilizing effects of margin-trading activity. Second, no evidence shows that short selling destabilizes the stock market in general. However, we observe that intensified short-selling activity is associated with lower return volatility when infection risk is high during the COVID-19 crisis.

H3K9 demethylase KDM4E is an epigenetic regulator for bovine embryonic development and a defective factor for nuclear reprogramming.

Aberrant epigenetic reprogramming often results in developmental defects in somatic cell nuclear transfer (SCNT) embryos during embryonic genome activation (EGA). Bovine eight-cell SCNT embryos exhibit global hypermethylation of histone H3 lysine 9 tri- and di-methylation (H3K9me3/2), but the intrinsic reason for this remains elusive. Here, we provide evidence that two H3K9 demethylase genes, lysine-specific demethylase 4D (KDM4D) and 4E (KDM4E), are related to active H3K9me3/2 demethylation in in vitro fertilized (IVF) embryos and are deficiently expressed in cloned embryos at the time of EGA. Moreover, KDM4E plays a more crucial role in IVF and SCNT embryonic development, and overexpression of KDM4E can restore the global transcriptome, improve blastocyst formation and increase the cloning efficiency of SCNT embryos. Our results thereby indicate that KDM4E can function as a crucial epigenetic regulator of EGA and as an internal defective factor responsible for persistent H3K9me3/2 barriers to SCNT-mediated reprogramming. Furthermore, we show that interactions between RNA and KDM4E are essential for H3K9 demethylation during EGA. These observations advance the understanding of incomplete nuclear reprogramming and are of great importance for transgenic cattle procreation.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. RESULTS: Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. AVAILABILITY AND IMPLEMENTATION: An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SynQuant: an automatic tool to quantify synapses from microscopy images.

MOTIVATION: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. RESULTS: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. AVAILABILITY AND IMPLEMENTATION: Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.