Genomic basis for RNA alterations in cancer.

Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.

The Important Role of the Right Dorsolateral Prefrontal Cortex in Conflict Adaptation: A Combined Voxel-Based Morphometry and Continuous Theta Burst Stimulation Study.

Humans can flexibly adjust their executive control to resolve conflicts. Conflict adaptation and conflict resolution are crucial aspects of conflict processing. Functional neuroimaging studies have associated the dorsolateral prefrontal cortex (DLPFC) with conflict processing, but its causal role remains somewhat controversial. Moreover, the neuroanatomical basis of conflict processing has not been thoroughly examined. In this study, the Stroop task, a well-established measure of conflict, was employed to investigate (1) the neuroanatomical basis of conflict resolution and conflict adaptation with the voxel-based morphometry analysis, (2) the causal role of DLPFC in conflict processing with the application of the continuous theta burst stimulation to DLPFC. The results revealed that the Stroop effect was correlated to the gray matter volume of the precuneus, postcentral gyrus, and cerebellum, and the congruency sequence effect was correlated to the gray matter volume of superior frontal gyrus, postcentral gyrus, and lobule paracentral gyrus. These findings indicate the neuroanatomical basis of conflict resolution and adaptation. In addition, the continuous theta burst stimulation over the right DLPFC resulted in a significant reduction in the Stroop effect of RT after congruent trials compared with vertex stimulation and a significant increase in the Stroop effect of accuracy rate after incongruent trials than congruent trials, demonstrating the causal role of right DLPFC in conflict adaptation. Moreover, the DLPFC stimulation did not affect the Stroop effect of RT and accuracy rate. Overall, our study offers further insights into the neural mechanisms underlying conflict resolution and adaptation.

Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions.

Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.

Task functional networks predict individual differences in the speed of emotional facial discrimination.

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.

Engineering 0D/2D Architecture of Ni(OH)2 Nanoparticles on Covalent Organic Framework Nanosheets for Selective Visible-Light-Driven CO2 Reduction.

Low-dimensional materials serving as photocatalysts favor providing abundant unsaturated active sites and shortening the charge transport distance, but the high surface energy readily causes the aggregation that limits their application. Herein, it is demonstrated that 2D covalent organic framework (COF) TpBD nanosheets are effective in the dispersion and stabilization of 0D Ni(OH)2 . The COF precursor TpBD is synthesized from the Schiff base condensation of 1,3,5-triformylphloroglucinol (Tp) and benzidine (BD) and exfoliated into 2D nanosheets named BDNs via ultrasonication. The formation of highly dispersive 0D Ni(OH)2 on BDNs is reached under a mild weak basic condition, enabling robust active sites for CO2 adsorption/activation and rapid interface cascaded electron transport channels for the accumulation of long-lived photo-generated charges. The champion catalyst 30%Ni-BDNs effectively catalyze the CO2 to CO conversion under visible-light irradiation, offering a high CO evolution rate of 158.4 mmol g-1 h-1 and turnover frequency of 51 h-1 . By contrast, the counterpart photocatalyst, the bulk TpBD stabilized Ni(OH)2 , affords a much lower CO evolution rate and selectivity. This work demonstrates a new avenue to simultaneously construct efficient active sites and electron transport channels by coupling 0D metal hydroxides and 2D COF nanosheets for CO2 photoreduction.

Substitutional Cd Dopant as Photohole Transfer Mediator Boosting Photoelectrochemical Solar Energy Conversion of 2D Cd-ZnIn2 S4 Photoanode.

Fast recombination dynamics of photocarriers competing with sluggish surface photohole oxidation kinetics severely restricts the photoelectrochemical (PEC) conversion efficiency of photoanode. Here, a defect engineering strategy is developed to regulate photohole transfer and interfacial injection dynamics of 2D ZnIn2 S4 (ZIS). Via selectively introducing substitutional Cd dopant at Zn sites of the ZIS basal plane, energy band structure and surface electrochemical activity are successfully modulated in the Cd-doped ZIS (Cd-ZIS) nanosheet array photoanode. Comprehensive characterizations manifest that a shallow acceptor level induced by Cd doping and superior electrochemical activity make surface Cd dopants simultaneously act as capture centers and active sites to mediate photohole dynamics at the reaction interface. In depth photocarrier dynamics analysis demonstrates that highly efficient photohole capture of Cd dopants brings about effective space separation of photocarriers and acceleration of surface reaction kinetics. Therefore, the optimum 2D Cd-ZIS achieves excellent PEC solar energy conversion efficiency with a photocurrent density of 5.1 mA cm-2 at 1.23 VRHE and a record of applied bias photon-to-current efficiency (ABPE) of 3.0%. This work sheds light on a microstructure design strategy to effectively regulate photohole dynamics for the next-generation semiconducting PEC photoanodes.

cTBS to Right DLPFC Modulates Physiological Correlates of Conflict Processing: Evidence from a Stroop task.

Conflict typically occurs when goal-directed processing competes with more automatic responses. Though previous studies have highlighted the importance of the right dorsolateral prefrontal cortex (rDLPFC) in conflict processing, its causal role remains unclear. In the current study, the behavioral experiment, the continuous theta burst stimulation (cTBS), and the electroencephalography (EEG) were combined to explore the effects of behavioral performance and physiological correlates during conflict processing, after the cTBS over the rDLPFC and vertex (the control condition). Twenty-six healthy participants performed the Stroop task which included congruent and incongruent trials. Although the cTBS did not induce significant changes in the behavioral performance, the cTBS over the rDLPFC reduced the Stroop effects of conflict monitoring-related frontal-central N2 component and theta oscillation, and conflict resolution-related parieto-occipital alpha oscillation, compared to the vertex stimulation. Moreover, a significant hemispheric difference in alpha oscillation was exploratively observed after the cTBS over the rDLPFC. Interestingly, we found the rDLPFC stimulation resulted in significantly reduced Stroop effects of theta and gamma oscillation after response, which may reflect the adjustment of cognitive control for the next trial. In conclusion, our study not only demonstrated the critical involvement of the rDLPFC in conflict monitoring, conflict resolution processing, and conflict adaptation but also revealed the electrophysiological mechanism of conflict processing mediated by the rDLPFC.

Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms for drug-target interactions prediction.

BACKGROUND: Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of drug discovery. A variety of non-negativity matrix factorization based methods are proposed to predict DTIs, but most of them overlooked the sparsity of feature matrices and the convergence of adopted matrix factorization algorithms, therefore their performances can be further improved. RESULTS: In order to predict DTIs more accurately, we propose a novel method iPALM-DLMF. iPALM-DLMF models DTIs prediction as a problem of non-negative matrix factorization with graph dual regularization terms and [Formula: see text] norm regularization terms. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and [Formula: see text] norm regularization terms are used to ensure the sparsity of the feature matrices obtained by non-negative matrix factorization. To solve the model, iPALM-DLMF adopts non-negative double singular value decomposition to initialize the nonnegative matrix factorization, and an inertial Proximal Alternating Linearized Minimization iterating process, which has been proved to converge to a KKT point, to obtain the final result of the matrix factorization. Extensive experimental results show that iPALM-DLMF has better performance than other state-of-the-art methods. In case studies, in 50 highest-scoring proteins targeted by the drug gabapentin predicted by iPALM-DLMF, 46 have been validated, and in 50 highest-scoring drugs targeting prostaglandin-endoperoxide synthase 2 predicted by iPALM-DLMF, 47 have been validated.

Synthesis and Crystal Structure of a New RTH-Type Precursor and Its Interlayer Expanded Zeolite.

Two dimensional zeolites have drawn a lot of attention due to their structural diversity and chemical composition, which can be used to obtain 3D zeolites, for which there is no direct synthesis. Here, a new layer silicate zeolite L was synthesized using the N, N-dimethyl-(2-methyl)-benzimidazolium as the organic structure-directing agent (OSDA) in the presence of fluoride. Structure determination by single-crystal X-ray diffraction reveals that the pure silica precursor with five-ring pores in the crystalline sheets is composed of the rth layer stacking along the (001) direction in an …AAAA… sequence with SDA+ cations and F- residing within the interlayer spaces. Variable temperature powder X-ray diffraction (PXRD) results showed that the new layer could transform into a 3D RTH topology structure at 350 °C via 2D-3D topotactic transformation. Furthermore, a new 3D zeolite material is obtained by treating the original layer with a diethoxydimethylsilane agent under hydrochloric acid condition (HCl-DEDMS). Based on the PXRD results and the original layer structure, the new 3D zeolite structure expanding the rth layer with another Si atom is constructed, which possesses a 10×8×6 channel system. It displays a high BET surface area of 188 cm3 /g with an external surface area of 130 cm3 /g. The structure and textural properties pave a way for potential catalytic applications. The research not only provides a new layered zeolite, broadening the 2D zeolite framework types, but also allows for the discovery of a new stable 3D zeolite expanding the RTH structure with Si atom, which hasn't been reported yet.

High expression of SDC1 in stromal cells is associated with good prognosis in colorectal cancer.

We have previously reported that patients with high Syndecan 1 (SDC1) expression in colorectal cancer (CRC) cells have a favorable prognosis, and we also found that stromal cells showed upregulation of SDC1, but the clinical significance is unclear. The expression of SDC1 in the stroma cells was assessed by immunohistochemistry using a tissue microarray comprising representative cores from 513 CRC patients. The correlation between the expression of SDC1 in the stroma cells and the clinicopathological features of patients was analyzed. The data showed that the expression of SDC1 in the stroma cells was correlated with the degree of differentiation ( P = 0.012) and tumor location (up or down) ( P = 0.005). Also, CRCs patients with high expression of SDC1 in the stromal cells have a good prognosis ( P = 0.0369). Accumulating evidence indicates that SDC1, whether in tumor cells or stromal cells, plays a tumor-suppressor role in CRCs.

Effects of virtual reality-based cue exposure therapy on craving and physiological responses in alcohol-dependent patients-a randomised controlled trial.

BACKGROUND: Cue exposure therapy is used to treat alcohol dependence. However, its effectiveness is controversial due to the limitations of the clinical treatment setting. Virtual reality technology may improve the therapeutic effect. The aim of this study is to explore whether virtual reality-based cue exposure therapy can reduce the psychological craving and physiological responses of patients with alcohol dependence. METHODS: Forty-four male alcohol-dependent patients were recruited and divided into the study group (n = 23) and the control group (n = 21) according to a random number table. The control group received only conventional clinical treatment for alcohol dependence. The study group received conventional clinical treatment with the addition of VR cue exposure (treatment). The primary outcome was to assess psychological craving and physiological responses to cues of patients before and after treatment. RESULTS: After virtual reality-based cue exposure therapy, the changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those in the control group (P < 0.05), while the changes in skin conductance and respiration between the study group and the control group were not significantly different (P > 0.05). The changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those before treatment (P < 0.05), while the changes in skin conductance and respiration were not significantly different from those before treatment (P > 0.05). The changes in VAS, heart rate, skin conductance and respiration before and after cue exposure in the control group were not significantly different from those before treatment (P > 0.05). CONCLUSION: Virtual reality-based cue exposure therapy can reduce the psychological craving and part of the physiological responses of alcohol-dependent patients during cue exposure in the short term and may be helpful in the treatment of alcohol dependence. TRIAL REGISTRATION: The study protocol was registered at the China Clinical Trial Registry on 26/02/2021 ( www.chictr.org.cn ; ChiCTR ID: ChiCTR2100043680).

ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Network for Multi-Step Traffic Forecasting.

Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks (GCNs) are widely used to extract spatial information from traffic networks. Existing GCNs for traffic forecasting are usually shallow networks that only aggregate two- or three-order node neighbor information. Because of aggregating deeper neighborhood information, an over-smoothing phenomenon occurs, thus leading to the degradation of model forecast performance. In addition, most existing traffic forecasting graph networks are based on fixed nodes and therefore need more flexibility. Based on the current problem, we propose Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Networks (ADSTGCN), a new traffic forecasting model. The model addresses over-smoothing due to network deepening by using dynamic hidden layer connections and adaptively adjusting the hidden layer weights to reduce model degradation. Furthermore, the model can adaptively learn the spatial dependencies in the traffic graph by building the parameter-sharing adaptive matrix, and it can also adaptively adjust the network structure to discover the unknown dynamic changes in the traffic network. We evaluated ADSTGCN using real-world traffic data from the highway and urban road networks, and it shows good performance.

Morphology Regulation of Zeolite MWW via Classical/Nonclassical Crystallization Pathways.

The morphology and porosity of zeolites have an important effect on adsorption and catalytic performance. In the work, simple inorganic salts, i.e., Na salts were used to synthesize MWW zeolite using the organic compound 1-Butyl-2,3-dimethyl-1H-imidazol-3-ium hydroxide as a structure-directing agent and the morphology was regulated by the alkali metals. The sample synthesized without Na salts shows a dense hexagon morphology, while different morphologies like ellipsoid, wool ball, and uniform hexagon appear when using NaOH, Na2CO3, and NaHCO3, respectively. Moreover, the impact of Na salts on the induction, nucleation, and the evolution of crystal growth was studied. Different kinds of Na salts have a different impact on the crystalline induction time in the order of NaHCO3 (36 h) < Na2CO3 (72 h) = NaOH (72 h). Meanwhile, the crystalline mechanism with the cooperation of inorganic salts and the organic SDAs is proposed. NaOH- and Na2CO3-MWW zeolite crystallized with a network of hydrogel via the nonclassical pathway in the system; however, the product is synthesized via a classical route in the NaHCO3 environment. This work provides information about MWW zeolite crystallization and modulating diverse morphologies by adjusting the process.

Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.

BACKGROUND: Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved. RESULTS: In order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.

Shared and distinct structure-function substrates of heterogenous distractor suppression ability between high and low working memory capacity individuals.

Salient stimuli can capture attention in a bottom-up manner; however, this attentional capture can be suppressed in a top-down manner. It has been shown that individuals with high working memory capacity (WMC) can suppress salient­but-irrelevant distractors better than those with low WMC; however, neural substrates underlying this difference remain unclear. To examine this, participants with high or low WMC (high-/low-WMC, n = 44/44) performed a visual search task wherein a color singleton item served as a salient distractor, and underwent structural and resting-state functional magnetic resonance imaging scans. Behaviorally, the color singleton distractor generally reduced the reaction time (RT). This RT benefit (ΔRT) was higher in the high-WMC group relative to the low-WMC group, indicating the superior distractor suppression ability of the high-WMC group. Moreover, leveraging voxel-based morphometry analysis, gray matter morphology (volume and deformation) in the ventral attention network (VAN) was found to show the same, positive associations with ΔRT in both WMC groups. However, correlations of the opposite sign were found between ΔRT and gray matter morphology in the frontoparietal (FPN)/default mode network (DMN) in the two WMC groups. Furthermore, resting-state functional connectivity analysis centering on regions with a structural-behavioral relationship found that connections between the left orbital and right superior frontal gyrus (hubs of DMN and VAN, respectively) was correlated with ΔRT in the high-WMC group (but not in the low-WMC group). Collectively, our work present shared and distinct neuroanatomical substrates of distractor suppression in high- and low-WMC individuals. Furthermore, intrinsic connectivity of the brain network hubs in high-WMC individuals may account for their superior ability in suppressing salient distractors.

Oxygen Vacancies and Interface Engineering on Amorphous/Crystalline CrOx -Ni3 N Heterostructures toward High-Durability and Kinetically Accelerated Water Splitting.

Manipulating catalytic active sites and reaction kinetics in alkaline media is crucial for rationally designing mighty water-splitting electrocatalysts with high efficiency. Herein, the coupling between oxygen vacancies and interface engineering is highlighted to fabricate a novel amorphous/crystalline CrOx -Ni3 N heterostructure grown on Ni foam for accelerating the alkaline hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Density functional theory (DFT) calculations reveal that the electron transfer from amorphous CrOx to Ni3 N at the interfaces, and the optimized Gibbs free energies of H2 O dissociation (ΔGH-OH ) and H adsorption (ΔGH ) in the amorphous/crystalline CrOx -Ni3 N heterostructure are conducive to the superior and stable HER activity. Experimental data confirm that numerous oxygen vacancies and amorphous/crystalline interfaces in the CrOx -Ni3 N catalysts are favorable for abundant accessible active sites and enhanced intrinsic activity, resulting in excellent catalytic performances for HER and OER. Additionally, the in situ reconstruction of CrOx -Ni3 N into highly active Ni3 N/Ni(OH)2 is responsible for the optimized OER performance in a long-term stability test. Eventually, an alkaline electrolyzer using CrOx -Ni3 N as both cathode and anode has a low cell voltage of 1.53 V at 10 mA cm-2 , together with extraordinary durability for 500 h, revealing its potential in industrial applications.

Visual Selective Attention P300 Source in Frontal-Parietal Lobe: ERP and fMRI Study.

Visual selective attention can be achieved into bottom-up and top-down attention. Different selective attention tasks involve different attention control ways. The pop-out task requires more bottom-up attention, whereas the search task involves more top-down attention. P300, which is the positive potential generated by the brain in the latency of 300 ~ 600 ms after stimulus, reflects the processing of attention. There is no consensus on the P300 source. The aim of present study is to study the source of P300 elicited by different visual selective attention. We collected thirteen participants' P300 elicited by pop-out and search tasks with event-related potentials (ERP). We collected twenty-six participants' activation brain regions in pop-out and search tasks with functional magnetic resonance imaging (fMRI). And we analyzed the sources of P300 using the ERP and fMRI integration with high temporal resolution and high spatial resolution. ERP results indicated that the pop-out task induced larger P300 than the search task. P300 induced by the two tasks distributed at frontal and parietal lobes, with P300 induced by the pop-out task mainly at the parietal lobe and that induced by the search task mainly at the frontal lobe. Further ERP and fMRI integration analysis showed that neural difference sources of P300 were the right precentral gyrus, left superior frontal gyrus (medial orbital), left middle temporal gyrus, left rolandic operculum, right postcentral gyrus, and left angular gyrus. Our study suggests that the frontal and parietal lobes contribute to the P300 component of visual selective attention.

Nlrp5 and Tle6 expression patterns in buffalo oocytes and pre-implantation embryos.

Maternal-effect genes (MEGs) accumulate in oocytes during oogenesis and mediate the pre-implantation embryo developmental programme until activation of the zygote genome. Nlrp5 and Tle6 are required for normal pre-implantation and embryonic development. However, the precise function of these MEGs in buffalo (Bubalus bubalis) remains to be elucidated. The aim of this study was to characterize Nlrp5 and Tle6 sequences and analyse their mRNA and protein expression patterns in somatic tissues, oocytes and pre-implantation embryos of buffalo. The coding sequences of each gene were successfully cloned and characterized. Real-time quantitative reverse transcription PCR results revealed an absence of Nlrp5 or Tle6 transcripts in somatic tissues, with the exception of ovary. Expression levels of Nlrp5 and Tle6 in oocytes increased from the germinal vesicle stage to metaphase II stage and then gradually decreased during morula and blastocyst stages. Protein expression patterns were confirmed by immunofluorescence analysis. This study lays a foundation for further validation of the function of MEGs in buffalo.

Preparation and Modification Technology Analysis of Ionic Polymer-Metal Composites (IPMCs).

As a new type of flexible smart material, ionic polymer-metal composite (IPMC) has the advantages of being lightweight and having fast responses, good flexibility, and large deformation ranges. However, IPMC has the disadvantages of a small driving force and short lifespan. Based on this, this paper firstly analyzes the driving mechanism of IPMC. Then, it focuses on the current preparation technology of IPMC from the aspects of electroless plating and mechanical plating. The advantages and disadvantages of various preparation methods are analyzed. Due to the special driving mechanism of IPMC, there is a problem of short non-aqueous working time. Therefore, the modification research of IPMC is reviewed from the aspects of the basement membrane, working medium, and electrode materials. Finally, the current challenges and future development prospects of IPMC are discussed.