Genome-wide identification of functional enhancers and their potential roles in pig breeding

Background:The pig is an economically important livestock species and is a widely applied large animal model in medical research.Enhancers are critical regulatory elements that have fundamental functions in evolution,development and disease.Genome-wide quantification of functional enhancers in the pig is needed.Results:We performed self-transcribing active regulatory region sequencing(STARR-seq)in the porcine kidney epithelial PK15 and testicular ST cell lines,and reliably identified 2576 functional enhancers.Most of these enhancers were located in repetitive sequences and were enriched within silent and lowly expressed genes.Enhancers poorly overlapped with chromatin accessibility regions and were highly enriched in chromatin with the repressive histone modification H3K9me3,which is different from predicted pig enhancers detected using ChIP-seq for H3K27ac or/and H3K4me1 modified histones.This suggests that most pig enhancers identified with STARR-seq are endogenously repressed at the chromatin level and may function during cell type-specific development or at specific developmental stages.Additionally,the PPP3CA gene is associated with the loin muscle area trait and the QKI gene is associated with alkaline phosphatase activity that may be regulated by distal functional enhancers.Conclusions:In summary,we generated the first functional enhancer map in PK15 and ST cells for the pig genome and highlight its potential roles in pig breeding.

Enhancers and hindrances to doctor-nurse interdisciplinary collaborative practice in Nigeria

The purpose of this study was to identify factors that enhance and hinder interdisciplinary collaborative practice (ICP) among doctors and nurses at the Nnamdi Azikiwe teaching hospital, Nnewi, southeast Nigeria. The study was a cross-sectional descriptive survey and the quantitative method of data collection was employed. The population was all doctors irrespective of area of specialty and all nurses employed and working in the hospital as at the time of study. Proportionate stratified and convenience sampling methods were used to select study participants according to their categories. Using validated structured questionnaire, data were collected from 110 doctors and 95 nurses in the teaching hospital on their perception on ICP and factors that enhance/hinder ICP. Data were analyzed using both descriptive and inferential statistics. Specifically, frequencies, percentages, standard deviation and graphic presentation were used for descriptive analysis of scores while the unpaired t test of mean score using Graph Pad Prism, Version 5.30 was used to determine the influence of profession, gender, and years of experience on perception of ICP at 0.05 level of significance. The study found that both doctors and nurses have positive perception on ICP. Their years of experience have significant influence on their perception. Clear individual roles and good working relationships enhance ICP while giving priority to professional status rather than expertise was seen as a prominent hindrance to ICP. The study recommends collaborative continuing education for doctors and nurses to enhance ICP in patient care. In addition, the inclusion of interdisciplinary collaborative practice programmme into the curriculum of medical and nursing students (where it does not exist) would go a long way to strengthen ICP and decrease hindrances when they graduate.

Novel chemical permeation enhancers for transdermal drug delivery

Transdermal drug delivery has been accepted as a potential non-invasive route of drug administration,with advantages of prolonged therapeutic action,decreased side effect,easy use and better patient compliance.However,development of transdermal products is primarily hindered by the low permeability of the skin.To overcome this barrier effect,numerous new chemicals have been synthesized as potential permeation enhancers for transdermal drug delivery.In this review,we presented an overview of the investigations in this field,and further implications on selection or design of suitable permeation enhancers for transdermal drug delivery were also discussed.

Influence of chemical penetration enhancers on skin permeability of ellagic acid-loaded niosomes

This study aimed to develop niosomes of ellagic acid(EA),a potent antioxidant phytochemical substance,for dermal delivery and to investigate the influence of chemical penetration enhancers on the physicochemical properties of EA-loaded niosomes.The EA niosomes were prepared by reverse phase evaporation method using Span 60,Tween 60 and cholesterol as vesicle forming agents and Solulan C24 as a steric stabilizer.Polyethylene glycol 400(PEG)was used as a solubilizer while dimethylsulfoxide(DMSO)or Nmethyl-2-pyrrolidone(NMP)was used as a skin penetration enhancer.It was found that the mean particle sizes of EA-loaded niosomes were in the range of 312e402 nm with PI values of lower than 0.4.The niosomes were determined to be spherical multilamellar vesicles as observed by transmission electron microscope and optical microscopy.All niosomes were stable after 4 months storage at 4
C.In vitro skin permeation through human epidermis revealed that the skin enhancers affected the penetration of EA from the niosomes at 24 h.The DMSO niosomes showed the highest EA amount in epidermis;whereas the NMP niosomes had the highest EA amount in the acceptor medium.Concomitantly,the skin distribution by confocal laser scanning microscopy showed the high fluorescence intensity of the DMSO niosomes and NMP niosomes at a penetration depth of between 30e90 mm(the epidermis layer)and 90e120 mm(the dermis layer)under the skin,respectively.From the results,it can be concluded that the DMSO niosomes are suitable for epidermis delivery of EA while the NMP niosomes can be used for dermis delivery of EA.

The untold story between enhancers and skeletal muscle development

Currently,enhancers have key transcriptional regulatory roles in muscle development.Skeletal muscle formation involves various molecules,and in animals,enhancers are one of the main types of transcriptional regulatory regions that are of great importance to regulate myogenic gene expression.In muscle development,enhancers can generate enhancer RNAs(eRNAs)that are involved in the regulation of gene transcription.The regulation of gene expression by eRNAs offers great potential in improving animal production traits.Herein we comprehensively review the roles of enhancers in muscle formation and its potential function in skeletal muscle development.This review will describe the future application of enhancers in skeletal muscle development and discuss the prospects that enhancer studies offer for agriculture,biotechnology,and animal breeding.

The enhancing effect and promoting mechanisms of the stereoisomeric monoterpene alcohol esters as enhancers for drugs with different physicochemical properties

To explore the structure-activity connections of amphiphilic permeation enhancers containing the length of the hydrophobic chains as well as the properties of the polar head,O-acylgeraniol and O-acylnerol derivatives were synthesized from geraniol/nerol(cis-isomer of geraniol) and pharmaceutical excipient acids in this research. Their promotion of the percutaneous absorption of three drugs as the model, flurbiprofen(FP), isosorbide dinitrate(ISDN) and donepezil(DNP), which were selected based on their physicochemical properties,was tested by in vitro skin penetration and in vivo. Molecular simulation, ATR-FTIR, CLSM and histological observation were implement to evaluate the mode of action of the enhancers.The results indicated that(E)-3,7-dimethyl-2,6-octadien-1-yl tetradecanoate(GER-C14, trans-)achieved the highest enhancement ability for the three drugs;additionally, the in vivo results obtained were in good correlation with the in vitro data. Molecular docking results suggested that enhancers loosen the hydrogen bonds between ceramides, and the results of molecular simulation indicated that GER-C14, NER-C14 could insert into the middle of the lipid bilayer to form an independent phase. According to ATR-FTIR and histological evaluation, the enhancers extracted lipids and influenced the protein region, thereby disturbing the skin array. In addition, CLSM described the dynamic effects of enhancers on lipids between stratum corneum(SC) cells. In conclusion, GER-C14 had a better penetration promotion effect, which broadened our understanding of stereoisomeric penetration enhancers.

Potential of Essential Oils as Alternative Permeation Enhancers for Transdermal Delivery

Transdermal drug delivery plays a significant part in the drug delivery system when compared to other routes of drug administration.The function of the stratum corneum(SC)is a barrier.Recently,numerous methods have been thrived to improve the perforation of drugs across the skin.The most effective method is to use enhancers since these agents enhance skin permeability.Natural penetration enhancers like essential oils demonstrate higher enhancement activity and are more widely accepted than synthetic penetration enhancers.High potential in the expansion and interaction with the SC intercellular lipids has led to an increasing interest in these oils as penetration enhancers.This article gives an overview of a few essential oils,including their mode of action and important parameters for permeation improvement.The present work can provide essential oils as alternative enhancers,and this could be useful in transdermal administration.

Steering Against Wind： A New Network of NamiRNAs and Enhancers

Micro RNAs(miRNAs) are a class of endogenous non-coding RNAs with regulatory functions. Traditionally, miRNAs are thought to play a negative regulatory role in the cytoplasm by binding to the 30 UTR of target genes to degrade m RNA or inhibit translation. However, it remains a challenge to interpret the potential function of many miRNAs located in the nucleus.Recently, we reported a new type of miRNAs present in the nucleus, which can activate gene expression by binding to the enhancer, and named them nuclear activating miRNAs(NamiRNAs). The discovery of NamiRNAs showcases a complementary regulatory mechanism of mi RNA, demonstrating their differential roles in the nucleus and cytoplasm. Here, we reviewed miRNAs in nucleus to better understand the function of NamiRNAs in their interactions with the enhancers. Accordingly, we propose a Nami RNA–enhancer–target gene activation network model to better understand the crosstalk between NamiRNAs and enhancers in regulating gene transcription.Moreover, we hypothesize that NamiRNAs may be involved in cell identity or cell fate determination during development, although further study is needed to elucidate the underlying mechanisms in detail.

Broadband Dual-Input Doherty Power Amplifier Design Based on a Simple Adaptive Power Dividing Ratio Function

In this paper,a simple adaptive power dividing function for the design of a dual-input Doherty power amplifier(DPA)is presented.In the presented approaches,the signal separation function(SSF)at different frequency points can be characterized by a polynomial.And in the practical test,the coefficients of SSF can be determined by measuring a small number of data points of input power.Same as other dualinput DPAs,the proposed approach can also achieve high output power and back-off efficiency in a broadband operation band by adjusting the power distribution ratio flexibly.Finally,a 1.5-2.5 GHz highefficiency dual-input Doherty power amplifier is implemented according to this approach.The test results show that the peak power is 48.6-49.7d Bm,and the 6-d B back-off efficiency is 51.0-67.0%,and the saturation efficiency is 52.4-74.6%.The digital predistortion correction is carried out at the frequency points of 1.8/2.1GHz,and the adjacent channel power ratio is lower than-54.5d Bc.Simulation and experiment results can verify the effectiveness and correctness of the proposed method.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Finesse measurement for high-power optical enhancement cavity

Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the advantages, disadvantages, and potential limitations of several main methods from both theoretical and practical perspectives. A variant of the existing method called the free spectral range(FSR) modulation method is proposed and compared with three other finesse measurement methods, i.e., the fast-switching cavity ring-down(CRD) method, the rapidly swept-frequency(SF) CRD method, and the ringing effect method. A high-power OEC platform with a high finesse of approximately 16000 is built and measured with the four methods. The performance of these methods is compared, and the results show that the FSR modulation method and the fast-switching CRD method are more suitable and accurate than the other two methods for high-finesse OEC measurements. The CRD method and the ringing effect method can be implemented in open loop using simple equipment and are easy to perform. Additionally, recommendations for selecting finesse measurement methods under different conditions are proposed, which benefit the development of OEC and its applications.

PAPS: Progressive Attention-Based Pan-sharpening

Pan-sharpening aims to seek high-resolution multispectral(HRMS) images from paired multispectral images of low resolution(LRMS) and panchromatic(PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images.Having a better MS base(enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at https://github.com/JiaYN1/PAPS.

Atomically Substitutional Engineering of Transition Metal Dichalcogenide Layers for Enhancing Tailored Properties and Superior Applications

Transition metal dichalcogenides(TMDs)are a promising class of layered materials in the post-graphene era,with extensive research attention due to their diverse alternative elements and fascinating semiconductor behavior.Binary MX2 layers with different metal and/or chalcogen elements have similar structural parameters but varied optoelectronic properties,providing opportunities for atomically substitutional engineering via partial alteration of metal or/and chalcogenide atoms to produce ternary or quaternary TMDs.The resulting multinary TMD layers still maintain structural integrity and homogeneity while achieving tunable(opto)electronic properties across a full range of composition with arbitrary ratios of introduced metal or chalcogen to original counterparts(0–100%).Atomic substitution in TMD layers offers new adjustable degrees of freedom for tailoring crystal phase,band alignment/structure,carrier density,and surface reactive activity,enabling novel and promising applications.This review comprehensively elaborates on atomically substitutional engineering in TMD layers,including theoretical foundations,synthetic strategies,tailored properties,and superior applications.The emerging type of ternary TMDs,Janus TMDs,is presented specifically to highlight their typical compounds,fabrication methods,and potential applications.Finally,opportunities and challenges for further development of multinary TMDs are envisioned to expedite the evolution of this pivotal field.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Enhanced structural damage behavior of liquid-filled tank by reactive material projectile impact

A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was studied in detail.PTFE/Al/W RMPs with steel-like and aluminum-like densities were prepared by a pressing/sintering process.The projectiles impacted a liquid-filled steel tank with front aluminum panel at approximately 1250 m/s.The corresponding cavity evolution characteristics and HRAM pressure were recorded by high-speed camera and pressure acquisition system,and further compared to those of steel and aluminum projectiles.Significantly different from the conical cavity formed by the inert metal projectile,the cavity formed by the RMP appeared as an ellipsoid with a conical front.The RMPs were demonstrated to enhance the radial growth velocity of cavity,the global HRAM pressure amplitude and the front panel damage,indicating the enhanced HRAM and structural damage behavior.Furthermore,combining the impact-induced fragmentation and deflagration characteristics,the cavity evolution of RMPs under the combined effect of kinetic energy impact and chemical energy release was analyzed.The mechanism of enhanced HRAM pressure induced by the RMPs was further revealed based on the theoretical model of the initial impact wave and the impulse analysis.Finally,the linear correlation between the deformation-thickness ratio and the non-dimensional impulse for the front panel was obtained and analyzed.It was determined that the enhanced near-field impulse induced by the RMPs was the dominant reason for the enhanced structural damage behavior.

Hyperspectral image super resolution using deep internal and self-supervised learning

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By consolidating the DIL and DSL into a unified deep framework,the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per obser-vation.To verify the effectiveness of the proposed approach,extensive experiments have been conducted on two benchmark HS datasets,including the CAVE and Harvard datasets,and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.

Carbon enhanced nucleophilicity of Na_(3)V_(2)(PO_(4))_(3):A general approach for dendrite-free zinc metal anodes

Zincophilic property and high electrical conductivity are both very important parameters to design novel Zn anode for aqueous Zn-ion batteries(AZIBs).However,single material is difficult to exhibit zincophilic property and high electrical conductivity at the same time.Herein,originating from theoretical calculation,a zincophilic particle regulation strategy is proposed to address these limitations and carbon coated Na_(3)V_(2)(PO_(4))_(3)is taken as an example to be a protective layer on zinc metal(NVPC@Zn).Na_(3)V_(2)(PO_(4))_(3)(NVP)is a common cathode material for Zn-ion batteries,which is zincophilic.Carbon materials not only offer an electron pathway to help Zn deposition onto NVPC surface,but also enhance the zinc nucleophilicity of Na_(3)V_(2)(PO_(4))_(3).Hence,this hybrid coating layer can tune zinc deposition and resist side reactions such as hydrogen generation and Zn metal corrosion.Experimentally,a symmetrical battery with NVPC@Zn electrode displays highly reversible plating/stripping behavior with a long cycle lifespan over 1800 h at2 mA cm^(-2),much better than carbon and Na_(3)V_(2)(PO_(4))_(3)solely modified Zn electrodes.When the Na_(3)V_(2)(PO_(4))_(3)is replaced with zincophobic Al2O3or zincophilic V2O3,the stability of the modified zinc anodes is also prolonged.This strategy expands the option of zincophilic materials and provides a general and effective way to stabilize the Zn electrode.

A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.

Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

This paper proposes a new network structure,namely the ProNet network.Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment.The baseline model of the ProNet network is UperNet(Unified perceptual parsing Network),and the backbone network is ConvNext(Convolutional Network).A network structure based on depth-separable convolution and 1×1 convolution is used,which has good performance and robustness.We further optimise ProNet mainly in two aspects.One is data enhancement using increased noise and slight angle rotation,which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model’s performance.Meanwhile,it can effectively expand the training data set,reduce the influence of noise and abnormal data in the data set on the model,and improve the accuracy and reliability of the model.Another is the loss function aspect,and we finally use the focal loss function.The focal loss function is well suited for complex tasks such as object detection.The function will penalise the loss carried by samples that the model misclassifies,thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy.From the experimental results,the evaluation metrics mIoU(mean Intersection over Union)enhanced by 4.47%,and mDice enhanced by 2.92% compared to the baseline network.Better generalization effects and more accurate image segmentation are achieved.