STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization.

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.

MOTIVATION: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects and dropout still poses obstacles in discovering grouped patterns for cell types by unsupervised learning and its alternative-semi-supervised learning that utilizes a few labeled cells as guidance for cell-type annotation. RESULTS: We propose a robust cell-type annotation method scSemiGCN based on graph convolutional networks. Built upon a denoised network structure that characterizes reliable cell-to-cell connections, scSemiGCN generates pseudo labels for unannotated cells. Then supervised contrastive learning follows to refine the noisy single-cell data. Finally, message passing with the refined features over the denoised network structure is conducted for semi-supervised cell-type annotation. Comparison over several datasets with six methods under extremely limited supervision validates the effectiveness and efficiency of scSemiGCN for cell-type annotation. AVAILABILITY AND IMPLEMENTATION: Implementation of scSemiGCN is available at https://github.com/Jane9898/scSemiGCN.

Cytomegalovirus antiviral resistance among participants in the phase 3 trial of letermovir vs valganciclovir prophylaxis in kidney transplant recipients.

BACKGROUND: In a phase 3 trial, letermovir was non-inferior to valganciclovir for CMV disease prophylaxis in CMV-seronegative (R-) kidney transplant recipients (KTRs) who received a kidney from a CMV-seropositive donor (D+). Genotypic antiviral resistance and CMV glycoprotein B (gB) genotype are reported. METHODS: Plasma samples with detectable CMV DNA were sequenced for presence of known letermovir and valganciclovir resistance-associated amino acid substitutions (RASs) encoded by CMV gene regions (UL51, UL56, UL89, UL54, UL97) and prevalence of gB (UL55) genotypes (gB1-gB5). RESULTS: 84 of 292 participants in the letermovir and 93 of 297 in the valganciclovir group had evaluable data for ≥1 gene target. Letermovir RASs were not detected in participants who received letermovir prophylaxis; however, 3 had valganciclovir RASs (pUL97). Twelve participants in the valganciclovir group had valganciclovir RASs (pUL54, pUL97); and 1 who did not receive letermovir during the trial also had letermovir RASs (pUL56). All but 1 participant responded to valganciclovir treatment irrespective of breakthrough CMV DNAemia or frequency of RASs. gB1 was the most frequent genotype across all participants and subgroups. CONCLUSION: Letermovir RASs were not detected in the letermovir group, supporting a low risk for development of resistance with letermovir prophylaxis in CMV D+R- KTRs. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov: NCT03443869, EudraCT: 2017-001055-30.

The neurophysiological mechanisms of medial prefrontal-perirhinal cortex circuit mediating temporal order memory decline in early stage of AD rats.

The temporal component of episodic memory has been recognized as a sensitive behavioral marker in early stage of Alzheimer's disease (AD) patients. However, parallel studies in AD animals are currently lacking, and the underlying neural circuit mechanisms remain poorly understood. Using a novel AppNL-G-F knock-in (APP-KI) rat model, the developmental changes of temporal order memory (TOM) and the relationship with medial prefrontal cortex and perirhinal cortex (mPFC-PRH) circuit were determined through in vivo electrophysiology and microimaging technique. We observed a deficit in TOM performance during the object temporal order memory task (OTOMT) in APP-KI rats at 6 month old, which was not evident at 3 or 4 months of age. Alongside behavioral changes, we identified a gradually extensive and aggravated regional activation and functional alterations in the mPFC and PRH during the performance of OTOMT, which occurred prior to the onset of TOM deficits. Moreover, coherence analysis showed that the functional connectivity between the mPFC and PRH could predict the extent of future behavioral performance. Further analysis revealed that the aberrant mPFC-PRH interaction mainly attributed to the progressive deterioration of synaptic transmission, information flow and network coordination from mPFC to PRH, suggesting the mPFC dysfunction maybe the key area of origin underlying the early changes of TOM. These findings identify a pivotal role of the mPFC-PRH circuit in mediating the TOM deficits in the early stage of AD, which holds promising clinical translational value and offers potential early biological markers for predicting AD memory progression.

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data.

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.

Learning representation for multiple biological networks via a robust graph regularized integration approach.

Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug-target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.

DecentTree: scalable Neighbour-Joining for the genomic era.

MOTIVATION: Neighbour-Joining is one of the most widely used distance-based phylogenetic inference methods. However, current implementations do not scale well for datasets with more than 10 000 sequences. Given the increasing pace of generating new sequence data, particularly in outbreaks of emerging diseases, and the already enormous existing databases of sequence data for which Neighbour-Joining is a useful approach, new implementations of existing methods are warranted. RESULTS: Here, we present DecentTree, which provides highly optimized and parallel implementations of Neighbour-Joining and several of its variants. DecentTree is designed as a stand-alone application and a header-only library easily integrated with other phylogenetic software (e.g. it is integral in the popular IQ-TREE software). We show that DecentTree shows similar or improved performance over existing software (BIONJ, Quicktree, FastME, and RapidNJ), especially for handling very large alignments. For example, DecentTree is up to 6-fold faster than the fastest existing Neighbour-Joining software (e.g. RapidNJ) when generating a tree of 64 000 SARS-CoV-2 genomes. AVAILABILITY AND IMPLEMENTATION: DecentTree is open source and freely available at https://github.com/iqtree/decenttree. All code and data used in this analysis are available on Github (https://github.com/asdcid/Comparison-of-neighbour-joining-software).

CRISPR-Cas3 and type I restriction-modification team up against blaKPC-IncF plasmid transfer in Klebsiella pneumoniae.

OBJECTIVE: We explored whether the Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas and restriction-modification (R-M) systems are compatible and act together to resist plasmid attacks. METHODS: 932 global whole-genome sequences from GenBank, and 459 K. pneumoniae isolates from six provinces of China, were collected to investigate the co-distribution of CRISPR-Cas, R-M systems, and blaKPC plasmid. Conjugation and transformation assays were applied to explore the anti-plasmid function of CRISPR and R-M systems. RESULTS: We found a significant inverse correlation between the presence of CRISPR and R-M systems and blaKPC plasmids in K. pneumoniae, especially when both systems cohabited in one host. The multiple matched recognition sequences of both systems in blaKPC-IncF plasmids (97%) revealed that they were good targets for both systems. Furthermore, the results of conjugation assay demonstrated that CRISPR-Cas and R-M systems in K. pneumoniae could effectively hinder blaKPC plasmid invasion. Notably, CRISPR-Cas and R-M worked together to confer a 4-log reduction in the acquisition of blaKPC plasmid in conjugative events, exhibiting robust synergistic anti-plasmid immunity. CONCLUSIONS: Our results indicate the synergistic role of CRISPR and R-M in regulating horizontal gene transfer in K. pneumoniae and rationalize the development of antimicrobial strategies that capitalize on the immunocompromised status of KPC-KP.

Time of day does not impact spinal serotonin levels in humans.

Spinal serotonin enables neuro-motor recovery (i.e., plasticity) in patients with debilitating paralysis. While there exists time of day fluctuations in serotonin-dependent spinal plasticity, it is unknown, in humans, whether this is due to dynamic changes in spinal serotonin levels or downstream signaling processes. The primary objective of this study was to determine if time of day variations in spinal serotonin levels exists in humans. To assess this, intrathecal drains were placed in seven adults with cerebrospinal fluid (CSF) collected at diurnal (05:00 to 07:00) and nocturnal (17:00 to 19:00) intervals. High performance liquid chromatography with mass spectrometry was used to quantify CSF serotonin levels with comparisons being made using univariate analysis. From the 7 adult patients, 21 distinct CSF samples were collected: 9 during the diurnal interval and 12 during nocturnal. Diurnal CSF samples demonstrated an average serotonin level of 216.6 ± $ \pm $ 67.7 nM. Nocturnal CSF samples demonstrated an average serotonin level of 206.7 ± $ \pm $ 75.8 nM. There was no significant difference between diurnal and nocturnal CSF serotonin levels (p = .762). Within this small cohort of spine healthy adults, there were no differences in diurnal versus nocturnal spinal serotonin levels. These observations exclude spinal serotonin levels as the etiology for time of day fluctuations in serotonin-dependent spinal plasticity expression.

M6A RNA Methylation-Mediated Dysregulation of AGAP2-AS1 Promotes Trastuzumab Resistance of Breast Cancer.

INTRODUCTION: Trastuzumab is commonly used to treat human epidermal growth factor receptor-2-positive (HER2+) breast cancer, but its efficacy is often limited by chemotherapy resistance. Recent studies have indicated that long non-coding RNAs (lncRNAs) play important roles in tumor progression and response to therapy. However, the regulatory mechanisms associating lncRNAs and trastuzumab resistance remain unknown. METHODS: Quantitative polymerase chain reaction was performed to detect the expression of related genes. Western blot and immunofluorescence assays were used to evaluate protein expression levels. A series of gain- or loss-of-function assays confirmed the function of AGAP2-AS1 in trastuzumab resistance, both in vitro and in vivo. RNA immunoprecipitation and pull-down analyses were conducted to verify the interaction between METTL3/YTHDF2 and lncRNA AGAP2-AS1. RESULTS: AGAP2-AS1 was upregulated in trastuzumab-resistant cells and SKBR-3R-generated xenografts in nude mice. Silencing AGAP2-AS1 significantly decreased trastuzumab-induced cytotoxicity both in vitro and in vivo. Furthermore, m6A methylation of AGAP2-AS1 was reduced in trastuzumab-resistant cells compared to that in parental cells. In addition, METTL3 increased m6A methylation of AGAP2-AS1, which finally induced the suppressed AGAP2-AS1 expression. Moreover, YTHDF2 was essential for METTL3-mediated m6A methylation of AGAP2-AS1. Functionally, AGAP2-AS1 regulated trastuzumab resistance by inducing autophagy and increasing ATG5 expression. CONCLUSION: we demonstrated that METTL3/YTHDF2-mediated m6A methylation increased the expression of AGAP2-AS1, which could promote trastuzumab resistance in breast cancer. AGAP2-AS1 regulates trastuzumab resistance by inducing autophagy. Therefore, AGAP2-AS1 may be a promising predictive biomarker and therapeutic target in patients with breast cancer.

Structural Elucidation of Glycosaminoglycans in the Tissue of Flounder and Isolation of Chondroitin Sulfate C.

Glycosaminoglycans (GAGs) are valuable bioactive polysaccharides with promising biomedical and pharmaceutical applications. In this study, we analyzed GAGs using HPLC-MS/MS from the bone (B), muscle (M), skin (S), and viscera (V) of Scophthalmus maximus (SM), Paralichthysi (P), Limanda ferruginea (LF), Cleisthenes herzensteini (G), Platichthys bicoloratus (PB), Pleuronichthys cornutus (PC), and Cleisthenes herzensteini (CH). Unsaturated disaccharide products were obtained by enzymatic hydrolysis of the GAGs and subjected to compositional analysis of chondroitin sulfate (CS), heparin sulfate (HS), and hyaluronic acid (HA), including the sulfation degree of CS and HS, as well as the content of each GAG. The contents of GAGs in the tissues and the sulfation degree differed significantly among the fish. The bone of S. maximus contained more than 12 µg of CS per mg of dry tissue. Although the fish typically contained high levels of CSA (CS-4S), some fish bone tissue exhibited elevated levels of CSC (CS-6S). The HS content was found to range from 10-150 ug/g, primarily distributed in viscera, with a predominant non-sulfated structure (HS-0S). The structure of HA is well-defined without sulfation modification. These analytical results are independent of biological classification. We provide a high-throughput rapid detection method for tissue samples using HPLC-MS/MS to rapidly screen ideal sources of GAG. On this basis, four kinds of CS were prepared and purified from flounder bone, and their molecular weight was determined to be 23-28 kDa by HPGPC-MALLS, and the disaccharide component unit was dominated by CS-6S, which is a potential substitute for CSC derived from shark cartilage.

Self-Powered Integrated Sensing System with In-Plane Micro-Supercapacitors for Wearable Electronics.

Self-powered integrated sensor with high-sensitivity physiological signals detection is indispensable for next-generation wearable electronic devices. Herein, a Ti3 C2 Tx /CNTs-based self-powered resistive sensor with solar cells and in-plane micro-supercapacitors (MSCs) is successfully realized on a flexible styrene-ethylene/butylene-styrene (SEBS) electrospinning film. The prepared Ti3 C2 Tx /CNTs@SEBS/CNTs nanofiber membranes exhibit high electrical conductivity and mechanical flexibility. The laser-assisted fabricated Ti3 C2 Tx /CNTs based-MSCs demonstrate a high areal energy density of 52.89 and 9.56 µWh cm-2 with a corresponding areal power density of 0.2 and 4 mW cm-2 . Additionally, the MSCs exhibit remarkable capacity retention of 90.62% after 10 000 cycles. Furthermore, the Ti3 C2 Tx /CNTs based-sensor exhibits real-time detection capability for human facial micro-expressions and pulse single under physiological conditions. The repeated bending/release tests indicate the long-time cycle stability of the Ti3 C2 Tx /CNTs based-sensor. Owing to the excellent sensing performance, the sensing array was also fabricated. It is believed that this work develops a route for designing a self-powered sensor system with flexible production, high performance, and human-friendly characteristics for wearable electronics.

Molybdenum Oxycarbide Supported Rh-Clusters with Modulated Interstitial C-O Microenvironments for Promoting Hydrogen Evolution.

Tuning the microenvironment and electronic structure of support materials is essential strategy to induce electron transfer between supports and active centers, which is of great importance in optimizing catalytic kinetics. In this study, the molybdenum oxycarbide supported Rh-clusters are synthesized with modulated interstitial C-O microenvironments (Rh/MoOC) for promoting efficient hydrogen evolution in water splitting. Both electronic structure characterizations and theoretical calculations uncover the apparent charge transfer from Rh to MoOC, which optimizes the d-band center, H2 O adsorption energy, and hydrogen binding energy, thus enhancing its intrinsic hydrogen-evolving activities. In addition, the co-occurrence of interstitial C and O atoms in MoOC supports plays a vital role in the dissociation reaction of water during the hydrogen-evolving process. Impressively, the Rh/MoOC exhibits excellent hydrogen-evolving activities in terms of exceptional turnover frequency values (11.4 and 39.41 H2 s-1 in alkaline and acidic media) and mass activities (21.3 and 73.87 A mg-1 in alkaline and acidic media) at an overpotential of 100 mV, which is more than 40 times higher than that of the benchmark commercial Rh/C catalysts. This work sheds new light on designing water dissociation materials that surpasses most of the reported catalysts.

IrPd Nanoalloy-Structured Bifunctional Electrocatalyst for Efficient and pH-Universal Water Splitting.

The lack of high efficiency and pH-universal bifunctional electrocatalysts for water splitting to hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) hinders the large-scale production of green hydrogen. Here, an IrPd electrocatalyst supported on ketjenblack that exhibits outstanding bifunctional performance for both HER and OER at wide pH conditions is presented. The optimized IrPd catalyst exhibits a specific activity of 4.46 and 3.98 A mgIr -1 in the overpotential of 100 and 370 mV for HER and OER, respectively, in alkaline conditions. When applied to the anion exchange membrane electrolyzer, the Ir44 Pd56 /KB catalyst shows a stability of >20 h at a current of 250 mA cm-2 for water decomposition, indicating promising prospects for practical applications. Beyond offering an advanced electrocatalyst, this work also guides the rational design of desirable bifunctional electrocatalysts for HER and OER by regulating the microenvironments and electronic structures of metal catalytic sites for diverse catalysis.

DeFusion: a denoised network regularization framework for multi-omics integration.

With diverse types of omics data widely available, many computational methods have been recently developed to integrate these heterogeneous data, providing a comprehensive understanding of diseases and biological mechanisms. But most of them hardly take noise effects into account. Data-specific patterns unique to data types also make it challenging to uncover the consistent patterns and learn a compact representation of multi-omics data. Here we present a multi-omics integration method considering these issues. We explicitly model the error term in data reconstruction and simultaneously consider noise effects and data-specific patterns. We utilize a denoised network regularization in which we build a fused network using a denoising procedure to suppress noise effects and data-specific patterns. The error term collaborates with the denoised network regularization to capture data-specific patterns. We solve the optimization problem via an inexact alternating minimization algorithm. A comparative simulation study shows the method's superiority at discovering common patterns among data types at three noise levels. Transcriptomics-and-epigenomics integration, in seven cancer cohorts from The Cancer Genome Atlas, demonstrates that the learned integrative representation extracted in an unsupervised manner can depict survival information. Specially in liver hepatocellular carcinoma, the learned integrative representation attains average Harrell's C-index of 0.78 in 10 times 3-fold cross-validation for survival prediction, which far exceeds competing methods, and we discover an aggressive subtype in liver hepatocellular carcinoma with this latent representation, which is validated by an external dataset GSE14520. We also show that DeFusion is applicable to the integration of other omics types.

springD2A: capturing uncertainty in disease-drug association prediction with model integration.

MOTIVATION: Drug repositioning that aims to find new indications for existing drugs has been an efficient strategy for drug discovery. In the scenario where we only have confirmed disease-drug associations as positive pairs, a negative set of disease-drug pairs is usually constructed from the unknown disease-drug pairs in previous studies, where we do not know whether drugs and diseases can be associated, to train a model for disease-drug association prediction (drug repositioning). Drugs and diseases in these negative pairs can potentially be associated, but most studies have ignored them. RESULTS: We present a method, springD2A, to capture the uncertainty in the negative pairs, and to discriminate between positive and unknown pairs because the former are more reliable. In springD2A, we introduce a spring-like penalty for the loss of negative pairs, which is strong if they are too close in a unit sphere, but mild if they are at a moderate distance. We also design a sequential sampling in which the probability of an unknown disease-drug pair sampled as negative is proportional to its score predicted as positive. Multiple models are learned during sequential sampling, and we adopt parameter- and feature-based ensemble schemes to boost performance. Experiments show springD2A is an effective tool for drug-repositioning. AVAILABILITY AND IMPLEMENTATION: A python implementation of springD2A and datasets used in this study are available at https://github.com/wangyuanhao/springD2A. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Efficient IAKF Approach for Indoor Positioning Drift Correction.

In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets.

A New Index Developed for Fast Diagnosis of Meteorological Roles in Ground-Level Ozone Variations.

China experienced worsening ground-level ozone (O3) pollution from 2013 to 2019. In this study, meteorological parameters, including surface temperature (T 2 ), solar radiation (SW), and wind speed (WS), were classified into two aspects, (1) Photochemical Reaction Condition (PRC = T 2 × SW) and (2) Physical Dispersion Capacity (PDC = WS). In this way, a Meteorology Synthetic Index (MSI = PRC/PDC) was developed for the quantification of meteorology-induced ground-level O3 pollution. The positive linear relationship between the 90th percentile of MDA8 (maximum daily 8-h average) O3 concentration and MSI determined that the contribution of meteorological changes to ground-level O-3 varied on a latitudinal gradient, decreasing from ∼40% in southern China to 10%-20% in northern China. Favorable photochemical reaction conditions were more important for ground-level O3 pollution. This study proposes a universally applicable index for fast diagnosis of meteorological roles in ground-level O3 variability, which enables the assessment of the observed effects of precursor emissions reductions that can be used for designing future control policies. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s00376-021-1257-x.

Interfacial Atom-Substitution Engineered Transition-Metal Hydroxide Nanofibers with High-Valence Fe for Efficient Electrochemical Water Oxidation.

Developing low-cost electrocatalysts for efficient and robust oxygen evolution reaction (OER) is the key for scalable water electrolysis, for instance, NiFe-based materials. Decorating NiFe catalysts with other transition metals offers a new path to boost their catalytic activities but often suffers from the low controllability of the electronic structures of the NiFe catalytic centers. Here, we report an interfacial atom-substitution strategy to synthesize an electrocatalytic oxygen-evolving NiFeV nanofiber to boost the activity of NiFe centers. The electronic structure analyses suggest that the NiFeV nanofiber exhibits abundant high-valence Fe via a charge transfer from Fe to V. The NiFeV nanofiber supported on a carbon cloth shows a low overpotential of 181 mV at 10 mA cm-2 , along with long-term stability (>20 h) at 100 mA cm-2 . The reported substitutional growth strategy offers an effective and new pathway for the design of efficient and durable non-noble metal-based OER catalysts.

Alteration of the PKA-CREB cascade in the mPFC accompanying prepulse inhibition deficits: evidence from adolescent social isolation and chronic SKF38393 injection during early adolescence.

Prepulse inhibition (PPI) refers to the inhibition of the startle reflex that occurs when the startling stimulus is preceded by a weak prestimulus. Altered adolescent mPFC circuitry induced by early-life adversity might be a key source of PPI deficits. The current study focused on variations in the cyclic AMP (cAMP)/protein kinase A (PKA)-cAMP-response element-binding protein (CREB) pathway in the medial prefrontal cortex (mPFC). We found a negative relationship between PPI and the PKA-CREB cascade during adolescence by employing both developmental and pharmacologic manipulations. Experiment 1, with the early adolescent social isolation model [postnatal days (PNDs), 21-34), displayed a disrupted PPI at PND 35 and significantly altered PKA, phosphorylated CREB (p-CREB) and the ratio of p-CREB to CREB. In particular, the level of p-CREB was negatively related to PPI performance. In Experiment 2, SKF38393, a well-characterized activator of adenylate cyclase and cAMP/PKA, was chronically injected during early adolescence (PNDs 28-34). We sought to mimic potential biochemical changes, particularly PKA activation, which is possibly altered by adolescent social isolation, and to determine if PPI was disrupted, similar to the disruption associated with adolescent social isolation. On PND 35, PPI deficits were detected, as well as increased PKA, marginally increased CREB and no change occurred in p-CREB or the ratio of p-CREB to CREB. In particular, PKA activity was negatively related to PPI performance. Although these results are limited in suggesting a causal link between PPI deficits and PKA-CREB signaling, they may help to elucidate the role played by PKA-CREB in the mPFC in regulating PPI.