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Hafez Al-Momani,1 Hadeel Albalawi,2 Dua’a Al Balawi,2 Khaled M Khleifat,3 Iman Aolymat,4 Saja Hamed,5 Borhan Aldeen Albiss,6 Ashraf I Khasawneh,1 Ola Ebbeni,7 Ayman Alsheikh,8 AbdelRahman M Zueter,9 Jeffrey Peter Pearson,10 Christopher Ward11 1Department of Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan; 2Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, 13133, Jordan; 3Biology Department, College of Science, Mutah University, Mutah, Karak, 61710, Jordan; 4Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan; 5Department of Pharmaceutics & Pharmaceutical Technology, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa, 13133, Jordan; 6Nanotechnology Institute, Jordan University of Science & Technology, Irbid, 22110, Jordan; 7Department of Pharmacology and Public Health, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan; 8Department of Medical Laboratory Sciences, Faculty of Allied Medical Sciences, Zarqa University, Zarqa, 13110, Jordan; 9Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, 13133, Jordan; 10Biosciences Institute, Newcastle University Medical School, Newcastle Upon Tyne, UK; 11Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle Upon Tyne, NE2 4HH UKCorrespondence: Hafez Al-Momani, Department of Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan, Email Hafez@hu.edu.joIntroduction: Given the increasing frequency of drug-resistant bacteria and the limited progress in developing new antibiotics, it is necessary to explore new methods of combating microbial infections. Nanoparticles, particularly silver nanoparticles (Ag-NPs), have shown exceptional antibacterial characteristics; however, elevated concentrations of Ag-NPs can produce noticeable levels of toxicity in mammalian cells.Aim: This study examined the potential synergistic effect of combining a low dosage of Ag-NPs and anti-pseudomonas drugs against Pseudomonas aeruginosa (ATCC strain) and eleven clinical isolates from cystic fibrosis patients.Methods: The Ag-NPs were chemically produced by utilizing a seed extract from Peganum Harmala and characterized via ultraviolet-visible spectroscopy and scanning electron microscopy. The broth microdilution technique was utilized to investigate the minimum inhibitory concentration (MIC) of Ag-NPs and eight antibiotics (Piperacillin, Ciprofloxacin, Levofloxacin, Meropenem, Amikacin, Ceftazidime, Gentamicin, Aztreonam). The fractional inhibitory concentration index (FICI) was determined via the checkerboard method to evaluate the synergistic effects of Ag-NPs and various antibiotics.Results: The biosynthesized Ag-NPs were uniformly spherical and measured around 15 nm in size. When combined with antibiotics, Ag-NP produced statistically significant reductions in the amount of antibiotics required to completely prevent P. aeruginosa growth for all strains. The findings revealed that the MIC of Ag-NPs was 15 ug/mL for all strains which decreased substantially when administered with antibiotics at a dose of 1.875– 7.5 ug/mL. The majority of Ag-NP and antibiotic combinations exhibited a synergistic or partially synergistic impact. This was particularly noticeable in combinations containing Meropenem, Ciprofloxacin, and Aztreonam (in which the FIC index was less than or equal to 0.5).Conclusion: The findings revealed that combining Ag-NPs with antibiotics was more effective than using Ag-NPs or antibiotics in isolation and that combinations of Ag-NPs and antimicrobial agents displayed synergistic activity against the majority of strains assessed.Keywords: cystic fibrosis CF, biofilm, Pseudomonas aeruginosa, nanoparticles, silver nanoparticles, antibiotic resistance, synergy

Abstract Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

Summary: MicroRNAs (miRNAs) play crucial roles in physiological functions and disease, but the regulation of their nuclear biogenesis remains poorly understood. Here, BioID on Drosha, the catalytic subunit of the microprocessor complex, reveals its proximity to splicing factor proline- and glutamine (Q)-rich (SFPQ), a multifunctional RNA-binding protein (RBP) involved in forming paraspeckle nuclear condensates. SFPQ depletion impacts both primary and mature miRNA expression, while other paraspeckle proteins (PSPs) or the paraspeckle scaffolding RNA NEAT1 do not, indicating a paraspeckle-independent role. Comprehensive transcriptomic analyses show that SFPQ loss broadly affects RNAs and miRNA host gene (HG) expression, influencing both their transcription and the stability of their products. Notably, SFPQ protects the oncogenic miR-17∼92 polycistron from degradation by the nuclear exosome targeting (NEXT)-exosome complex and is tightly linked with its overexpression across a broad variety of cancers. Our findings reveal a dual role for SFPQ in regulating miRNA HG transcription and stability, as well as its significance in cancers.

Summary: Adult stem cells play a critical role in tissue repair and maintenance. In tissues with slow turnover, including skeletal muscle, these cells are maintained in a mitotically quiescent state yet remain poised to re-enter the cell cycle to replenish themselves and regenerate the tissue. Using a panomics approach we show that the PAX7/NEDD4L axis acts against muscle stem cell activation in homeostatic skeletal muscle. Our findings suggest that PAX7 transcriptionally activates the E3 ubiquitin ligase Nedd4L and that the conditional genetic deletion of Nedd4L impairs muscle stem cell quiescence, with an upregulation of cell cycle and myogenic differentiation genes. Loss of Nedd4L in muscle stem cells results in the expression of doublecortin (DCX), which is exclusively expressed during their in vivo activation. Together, these data establish that the ubiquitin proteasome system, mediated by Nedd4L, is a key contributor to the muscle stem cell quiescent state in adult mice.

IntroductionIn 2017, the Ministry of Health and Public Hygiene (MoH) of Burkina Faso designed and piloted a specimen transport system using the national courier services (La Poste BF) in 4 districts. Based on satisfactory performance indicators, the MoH set a vision aimed at scaling up this system to strengthen disease detection and surveillance of epidemic prone diseases across the country. This work describes the implementation process, performances, and lessons learned.MethodologyThis work describes the implementation process, performances, and lessons learned. Under the leadership of the Directorate of Population Health Protection within the MoH, a stepwise approach was used to bring together multiple partners across sectors to develop the first needed documents including a guide, an implementation plan, Standard Operating Procedures, and data collection tools. Then, the execution phase included equipment purchase, trainings, and consensus on a financing mechanism. Key indicators were defined to allow performance monitoringResultThe integrated biological specimen referral system (SITEB) was officially launched in January 2020 to transport human biological specimens of priority diseases including COVID-19 from district level to reference laboratories nationwide. As of December 31, 2022, La Poste BF transported 168,856 packages containing 206,314 specimens from all 13 regions. 99.66% of packages were delivered in

The rapid advancements in wireless communications have prompted a surge in mobile data traffic, necessitating innovative solutions for 5G and beyond. This paper introduces a two-tier Heterogeneous Cloud Radio Access Network (HC-RAN) model leveraging millimeter Wave (mmWave) and sub-6 GHz frequencies to address this need. It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. The study evaluates SINR and rate coverage probabilities across various deployment scenarios, including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, as well as random and edge-based deployments. Results demonstrate that strategic placement of Remote Radio Heads (RRHs) and efficient clustering significantly improve network efficiency and user connectivity. In LOS conditions, random RRH deployments deliver superior coverage and throughput due to spatial diversity and reduced path loss. Conversely, edge-based deployments necessitate more resources to handle traffic demands but can excel in controlled scenarios. The proposed joint User-RRH association with RRH-BBU k-means clustering algorithm effectively manages interference, also maintains a balance between quality of service and efficient resource management. The proposed User-RRH association sub problem scheme that based on minimum path loss as a basic criterion outperforms on Limited Capacity User-RRH Association scheme (LC UA) in both the random and edge deployment scenarios and yield increasing in average throughput by approximately 38% and 27%, respectively. In other hand, the adaptive solution of RRH-BBU k-means clustering sub problem depend on actual load and number of active RRHs in the network to find the number of k RRH-BBU clusters, which manage resource consumption. This highlights the challenges in resource allocation and management with and without clustering. This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. These insights help network operators balance high service quality with efficient resource utilization.

Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. The exchange of cryptographic keys between network devices plays a pivotal and critical role in facilitating secure communication and ensuring the integrity of cryptographic procedures. This essential process is time-consuming, which causes delay and discontinuity of data flow. To overcome delay or interruption problems, we utilized the Software-Defined Network (SDN), Machine Learning (ML), and Blockchain (BC) techniques, which support the Tor network to intelligently speed up exchanging the public key via the proactive processing of the Tor network security management information. Consequently, the combination network (ITor-SDN) keeps data flow continuity to a Tor client. We simulated and emulated the proposed network by using Mininet and Shadow simulations. The findings of the performed analysis illustrate that the proposed network architecture enhances the overall performance metrics, showcasing a remarkable advancement of around 55%. This substantial enhancement is achieved through the seamless execution of the innovative ITor-SDN network combination approach.

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a, and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

SDNs provide flexible and intelligent network capabilities by dividing traditional networks into a centralized control plane and a programmable data plane. Network performance improvement relies on the intelligent control plane, which establishes flow paths for switches. Within the control plane, the controller serves as the core component for all data plane management operations, underscoring the significance of its performance and capabilities. Additionally, it is crucial to utilize accurate and efficient tools for assessing various evaluation parameters. Specifically, based on their capabilities, we identify and categorize 14 controllers, offering a qualitative comparison of their features. Furthermore, we investigate the capabilities of benchmarking tools used to assess controllers for SDN.

Aging leads to significant alteration in the gene expression of muscle stem cells. In vivo exposure of muscle stem cells from aged mice to a young niche environment restores the expression of a significant portion of age-altered genes in mice.

Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges.

Massive Multiple Input Multiple Output system and physical layer network coding (PNC) are two important technologies that are considered in new generation networks. In this paper we investigate the effect of increasing the number of antenna elements at user nodes and relay node in the presence of PNC. Two-way wireless relay transmission is considered in conjunction with massive number of antennas at the relay node assuming millimeter wave (mmWave) band channel environment. End-to-end transmission is considered using 3-D QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) channel model. The performance measures used are the bit error rate (BER) and throughput. Up to 100% increase in throughput of PNC coded system is obtained. The BER performance is improved only when the number of relay node antennas is much higher than that of the user node. Combining mMIMO with PNC gives better throughput without affecting the BER at high SNR. This is achieved with reasonable number of antennas at the user nodes.

The role of mRNA stability in shaping the cancer transcriptome is revealed using a statistical analysis of transcriptomic data.

IntroductionAccessibility and safe movement in urban areas entail infrastructure that minimizes the risks for pedestrians and bikers with diverse levels of abilities. Recognizing and mapping unsafe areas can increase awareness among citizens and inform city projects to improve their infrastructure. This contribution presents an example in which the specific objective is to recognize the unprotected areas around the canals in the city of Amsterdam.MethodThis is accomplished through running image processing algorithms on 11K waterside panoramas taken from the city of Amsterdam's open data portal. We created an annotated subset of 2K processed images for training and evaluation. This dataset debuts a novel pixel-level annotation style using multiple lines. To determine the best inference practice, we compared the IoU and robustness of several existing segmentation frameworks.ResultsThe best method achieves an IoU of 0.79. The outcome is superimposed on the map of Amsterdam, showing the geospatial distribution of the low, middle, and high fences around the canals.DiscussionIn addition to this specific application, we discuss the broader use of the presented method for the problem of “transparent object detection” in an urban context.

Enhancing the torsional capacity of reinforced concrete (RC) beams while maintaining reasonably economic and efficient cross-sections poses a practical challenge in the design of such elements. Thus, developing and testing economically viable reinforcing techniques for enhancing the torsional capacity of RC beams is a significant aspect. Among these techniques is the continuous spiral reinforcement system that has shown promising results in enhancing the torsional capacity of rectangular RC elements.In this study, the role of continuous rectangular spiral stirrups, as a transverse reinforcement with a controllable pitch (P) and inclination angle (θ), in enhancing the torsional behavior of rectangular solid and hollow RC beams has been investigated experimentally. The applied torque was directed to lock the spirals for all specimens. The experimental program of this investigation included testing of ten specimens of normal strength RC beams; two of which were solid with closed and spiral rectangular stirrups. While the rest of specimens were hollow specimens included one beam reinforced by closed stirrups and seven beams reinforced by rectangular spiral stirrups with different reinforcing ratios. Different variables were considered to develop a wide range of the beams’ reinforcing scenarios. This includes stirrups type (conventional closed stirrups and rectangular spiral stirrups), inclined stirrups ratio, pitch, inclination angle and finally the longitudinal reinforcement ratio. In addition, a three-dimensional finite element model using ANSYS-15.0 was developed to examine the capability of the model to capture the observed torsional behavior of the experimentally tested beams.The obtained results showed that using inclined spiral rectangular stirrups in reinforcing RC solid and hollow beams greatly enhanced the torsional capacity by about 16% and 18%, respectively compared to using the conventional closed stirrups. Moreover, the results showed that using that technique leads to higher twist- angles, more ductile failure and increased strain energy by about 27% and 16% for solid and hollow beams, respectively. Lastly, the comparison between the results obtained from numerical model and experimental results showed good agreement and thus the numerical model can be used to extend the experimental work for investigating different reinforcing schemes.

Abstract Background While methylation of CpG dinucleotides is traditionally considered antagonistic to the DNA-binding activity of most transcription factors (TFs), recent in vitro studies have revealed a more complex picture, suggesting that over a third of TFs may preferentially bind to methylated sequences. Expanding these in vitro observations to in vivo TF binding preferences is challenging since the effect of methylation of individual CpG sites cannot be easily isolated from the confounding effects of DNA accessibility and regional DNA methylation. Thus, in vivo methylation preferences of most TFs remain uncharacterized. Results We introduce joint accessibility-methylation-sequence (JAMS) models, which connect the strength of the binding signal observed in ChIP-seq to the DNA accessibility of the binding site, regional methylation level, DNA sequence, and base-resolution cytosine methylation. We show that JAMS models quantitatively explain TF occupancy, recapitulate cell type-specific TF binding, and have high positive predictive value for identification of TFs affected by intra-motif methylation. Analysis of 2209 ChIP-seq experiments results in high-confidence JAMS models for 260 TFs, revealing a negative association between in vivo TF occupancy and intra-motif methylation for 45% of studied TFs, as well as 16 TFs that are predicted to bind to methylated sites, including 11 novel methyl-binding TFs mostly from the multi-zinc finger family. Conclusions Our study substantially expands the repertoire of in vivo methyl-binding TFs, but also suggests that most TFs that prefer methylated CpGs in vitro present themselves as methylation agnostic in vivo, potentially due to the balancing effect of competition with other methyl-binding proteins.

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, and 6.6% less energy compared with SA, GA, PSO, BFA, ACO, BA, and GSP, respectively.

In recent years, with the advent of 6G and intelligent devices, sensors, and new applications such as virtual reality and autonomous driving, user data traffic has exploded, especially video traffic and small IoT packets. These bandwidth-hungry applications require an increased network capacity and user access. The 6G network may use nonorthogonal multiple access (NOMA) instead of orthogonal multiple access (OMA) to maintain higher data rates, throughput, and lower latency. On the other hand, choosing the channel coding method for future 6G mobile communication is critical for maintaining the high demand for 6G. This paper proposes two-channel coding structures to achieve higher data rates with a lower error rate floor; these structures are polar convolutional serial code (PCSC) and polar convolutional parallel code (PCPC); these structures can achieve a larger channel capacity and reduced bit error rates when used with NOMA. The obtained simulation results showed that bit error rate (BER) performance improves the overall coding gain by 1.2 dB compared to polar code in fifth-generation (5G). PCSC surpasses PCPC with a 1.5dB coding gain. This performance ranged from 4 to 6.25dB with higher system settings. The obtained throughput results showed an improvement of 56-60%, in which the enhancement percentage depended on the modulation method used in a direct proportion manner.

Recently, the increasing demand to transfer data through the Internet has pushed the Internet infrastructure to the final edge of the ability of these networks. This high demand causes a deficiency of rapid response to emergencies and disasters to control or reduce the devastating effects of these disasters. As one of the main cornerstones to address the data traffic forwarding issue, the Internet networks need to impose the highest priority on the special networks: Security, Health, and Emergency (SHE) data traffic. These networks work in closed and private domains to serve a group of users for specific tasks. Our novel proposed network flow priority management based on ML and SDN fulfills high control to give the required flow priority to SHE data traffic. The proposal relies on selected header bits from the traffic class field of a packet using the ML to prioritize traffic flows according to the precedence levels by governing the Differentiated Services Code Point (DSCP) bits in keeping with network administrator policies. The proposed network has been evaluated and performed utilizing the MATLAB platform and the Mininet simulator. The results of extensive testing show enhancement by applying our forcing priority algorithm obtained an efficient reduction in queuing delay and lost packets. The average waiting time in queue was reduced by around 61%, and the lost packets hit 0.005% when adopting the SDN-based ML network traffic priority management.

Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill this gap, this paper proposes Intelligent SDN Multi Spike Neural System (IMSNS) by implementing Moderately Multi-Spike Return Neural Networks (MMSRNN) controller with time based coding achieving remarkable reduction on energy consumption and accurate traffic identification to predict the most appropriate network slice. In addition, this paper proposes another intelligent Recurrent Neural Network (RNN) controller for load balancing and slice failure condition. The current researchers have adopted the: accuracy, precision, recall and F1-Score, the simulation results revealed that the proposed model could provide the accurate 5G network slicing as compared with a convolutional neural network (CNN) by 5%.

Acinar-to-ductal metaplasia (ADM) is a recently recognized, yet less well-studied, precursor lesion of pancreatic ductal adenocarcinoma (PDAC) developed in the setting of chronic pancreatitis. Through digital spatial mRNA profiling, we compared ADM and adjacent PDAC tissues from patient samples to unveil the bridging genes during the malignant transformation of pancreatitis. By comparing the bridging genes with the 7-methylguanosine (m7G)-seq dataset, we screened 19 m7G methylation genes for a subsequent large sample analysis. We constructed the “m7G score” model based on the RNA-seq data for pancreatic cancer in The Cancer Genome Atlas (TCGA) database and The Gene Expression Omnibus (GEO) database. Tumors with a high m7G score were characterized by increased immune cell infiltration, increased genomic instability, higher response rate to combined immune checkpoint inhibitors (ICIs), and overall poor survival. These findings indicate that the m7G score is associated with tumor invasiveness, immune cell infiltration, ICI treatment response, and overall patients’ survival. We also identified FN1 and ITGB1 as core genes in the m7Gscore model, which affect immune cell infiltration and genomic instability not only in pancreatic cancer but also in pan-cancer. FN1 and ITGB1 can inhibit immune T cell activition by upregulation of macrophages and neutrophils, thereby leading to immune escape of pancreatic cancer cells and reducing the response rate of ICI treatment.

Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors’ data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks’ trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks’ sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.

Sample index hopping results in various artefacts in multiplexed scRNA-seq experiments. Here, the authors introduce a statistical model to estimate sample index hopping rate in droplet-based scRNA-seq data and show that artifacts can be corrected by purging phantom molecules from the data.

The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers’ load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation.

Quantum computing can provide a solution for many problems that classical communication networks encounter, but the knowledge gap between these two is not bridged yet. Accordingly, this paper has proposed directions, evaluations, and futuristic quantum solutions to cloud-based cellular communications. This paper shows the effect of using quantum entanglement phenomena upon the classical cloud network, particularly using an entanglement theory to decrease the enlarged signaling cost delay that the traditional cloud network faces. Through modeling the latency of both paradigms, this paper promises a delay reduction when adapting a quantum method into the mobile cloud networks. This paper also models the power consumption (PC) and energy efficiency of the traditional and quantum-based cloud networks. This paper also shows, via modeling the PC, that installing a quantum-based paradigm is not a power costly method; rather, it shows identical power and energy efficiency figures with a possibility of improvement.

The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses. These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior. In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed. Specifically, a modified structure of the Elman neural network with spike neural networks is proposed. This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm. This self-feedback has weights, which can be trained during the training process. The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error.

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.

This paper presents a proposal for an innovative base station for 5G and beyond for a mobile network system based on the evolved packet system (EPS). Functions of EPS elements can be achieved by the SVeNB, according to the type of function or service and the device that is used to achieve that function or service. The current system, even if these functions and services have been virtualized by the system, it may put them away from the users. Consequently, this causes a delay in completing the establishment of a connection. Since these functions and services can be virtualized and programmed in virtual machines (VMs), we have been simulated the kernel of those functions and services executed by the SVeNB through its VMs. Moreover, The SVeNB position is located close to the users, which helps to reduce the End-to-End delay. Our proposed approach gives the SVeNB the ability to share and contribute in making decisions with the core network (CN) and BaseBand Unit (BBU) pool to initiate a connection, further in making determinations within its vision regarding its users. This procedure leads to a reduction in the load on the BBU pool and the CN, thus lessening the latency, and to increase the data rate that is delivered to the users. That means the SVeNB can regionally decide for all local users that are covered by the SVeNB to connect to each other or the Internet. The MATLAB platform has been used to evaluate the SVeNB performance, with the results demonstrating that it surpasses. The obtained results point that the SVeNB passes the C-RAN system by 68% in terms of user profiles processing, whilst also and it reducing the End-to-End delay by 62%.

Keeping a connection continuity during the movement of a mobile node (MN) between access points without any suspension of provided services is one of the most pressing issues should be solved. Long handover processing causes interruptions in session connection, high rate of data loss, and long end-to-end delay time. Smart virtualization means the cooperation of different virtualization technologies with novel ideas. In this paper, we proposed a mobile network architecture compatible with cloud computing of 5G and beyond networks. We invented a new idea to create a tag to be used as an MN's identity, which consists of the standard E.164 numbering and MAC address. Based on the uniqueness of E.164 numbering and MAC which are processed together to generate the MN tag (TH). TH is used to handle the packets inside the mobile networks. The software-defined networking (SDN) provides a capability of separating the control plane from the data plane. This decoupling is a suitable candidate to exploit it in our proposed system that uses the SDN and other virtualization technologies. The requirements of the 5G and beyond for future mobile communications encouraged us to think in a novel packet forwarding during the handover to keep real-time connection continuity for an MN. Our proposed system has been simulated and performed by MATLAB and Mininet platforms. The results showed that the packet loss rate decreased to 4% of the date that was lost during the handover delay time or packets re-direction mechanism. At the same time, the MN could receive 96.4% of the data that were lost during the handover process.

The Internet of Things (IoT) comprises a large number of sensor nodes with limited processing, storage, and battery abilities. The IoT has to operate in a constrained environment with specific challenges, such as hardware malfunctions, battery depletion, and harsh wireless environmental conditions. Deploying a reliable IoT is especially important for critical IoT applications such as smart cities. To ensure the quality of service requirements of these applications, the IoT needs to provide specific reliability guarantees. There are several strategies to ensure energy efficient and reliable transport of data in the IoT. However, there is an inherent conflict between power consumption and reliability: an increase in reliability usually leads to an increase in power consumption as in traditional retransmission-based reliability. To solve this problem, we present four scenarios of optimization using a mixed integer linear programming (MILP) model. First, we used a standby routes selection scheme (SBRS) to replace node failures and achieve reliability with minimum traffic power consumption. Second, we used a desired reliability level scheme (DRLS), which minimizes the traffic power consumption of the IoT devices while considering the desired reliability level as a key factor. We also propose a reliability-based sub-channel scheme (RBS) to avoid overhead on busy reliable routes while mitigating interference. Moreover, we present a reliability-based data compression scheme (RBDS) to overcome the capacity limits of the links. The results show that our proposed schemes reduce the negative effect between reliability and total traffic power consumption with an average power saving of 57% in SBRS and 60% in RBDS compared to DRLS.

Summary: Widespread remodeling of the transcriptome is a signature of cancer; however, little is known about the post-transcriptional regulatory factors, including RNA-binding proteins (RBPs) that regulate mRNA stability, and the extent to which RBPs contribute to cancer-associated pathways. Here, by modeling the global change in gene expression based on the effect of sequence-specific RBPs on mRNA stability, we show that RBP-mediated stability programs are recurrently deregulated in cancerous tissues. Particularly, we uncovered several RBPs that contribute to the abnormal transcriptome of renal cell carcinoma (RCC), including PCBP2, ESRP2, and MBNL2. Modulation of these proteins in cancer cell lines alters the expression of pathways that are central to the disease and highlights RBPs as driving master regulators of RCC transcriptome. This study presents a framework for the screening of RBP activities based on computational modeling of mRNA stability programs in cancer and highlights the role of post-transcriptional gene dysregulation in RCC. : Perron et al. develop a computational approach that models the functional activity of RBPs in individual cancer samples by monitoring their associated RNA stability programs. Applying this method to renal cell carcinoma transcriptomes, the authors identify RBPs that enhance cancer-associated pathways including hypoxia and cell cycle. Keywords: RNA-binding proteins, gene regulation, mRNA stability, renal cancer, regulatory networks, network modeling, MBNL2, PCBP2, ESRP2

Analysing data centers energy consumption is the main step toward building a reliable infrastructure. Evidently, data centers consume a large number of billions of gigabytes information to the point that putting tremendous pressure on energy suppliers. Every internet activity involves a huge amount of data that need to be stored in a cloud data center somewhere, not forgetting the Internet of Things applications and other social media services that produce an extraordinarily large scale of big data that require high processing and analysis. Moreover, current data centers consume about 3% of global electricity supply, which is about 416.2 TWh of power that world data centers consumed in 2016. In this paper, we have developed an intelligent power reduction decision routing protocol (iPRDR) in a medium scale hybrid-software-defined network data center environment. The proposed iPRDR protocol approach is to dynamically segregate big traffic and route it to a high index processing devices with a power-optimal selected path. The protocol approach is to decrease the overall power consumption of the whole network, as well as to reduce the failure rate in each device that may occur due to a high level of link congestion and elevated temperature. The experimental results show that uplink utilization has been reduced by 8.33% and power consumption levels reduced by 0.85kw/day, which is equivalent to 34.9% of the operational power. In Addition, the high-raised temperatures have been dropped from high range 50+ C° (high critical) to mid 40+ C° (high warning). The effectiveness of the proposed approach was verified experimentally using a virtualized testbed platform.

Although wind-generated power was estimated to be 4% of the entire world electricity usage, wind turbines are considered to be a growing technology with many experts considering new approaches to wind turbine design and farmland selection to increase the wind turbine output efficiency. When implementing a new wind turbine install on a farm, many concerns are taken into consideration such as environmental challenges and cost. Thus, the power of the wind turbine can be increased at least 10 times when the most efficient wind power location is selected before the wind turbine is placed. In this paper, we propose a novel software-defined quadcopter-6 wheeled industrial IoT (SDQ-6WI) architecture that is based on a developed quadcopter system to collect wind speed data from a mobile IoT vehicle base station that is based on a developed open flow (DOV) protocol operation. The mobile ground vehicle act as a wind speed measurement system that travels on a given set of waypoints to measure the best optimal wind speed quality and send the collected information to the quadcopter-based SDN controller then to the cloud for further processing. Our proposed system can handle a heterogeneous environment that lacks Wi-Fi and cellular coverage and uses the minimum total transmission power when sending data. The experiential results showed that the measured wind speed data could be collected in a time-efficient manner compared to a traditional process which is considered to be costly, time wasting, and non-effective. Our extensive testing showed that about 23.19% in power was reduced in the wind measurement process in comparison with the fixed sensor nodes. In essence, the proposed architecture help reduce the high cost of relocating wind turbines to an efficient location and increase the generated power by selecting the best optimal windy location.

“mRNA abundance is determined by the rates of transcription and decay. Here, the authors propose a method for estimating the rate of differential mRNA decay from RNA-seq data and model mRNA stability in the brain, suggesting a link between mRNA stability and Alzheimer’s disease.”

Abstract Background The C2H2 zinc finger (C2H2-ZF) is the most numerous protein domain in many metazoans, but is not as frequent or diverse in other eukaryotes. The biochemical and evolutionary mechanisms that underlie the diversity of this DNA-binding domain exclusively in metazoans are, however, mostly unknown. Results Here, we show that the C2H2-ZF expansion in metazoans is facilitated by contribution of non-base-contacting residues to DNA binding energy, allowing base-contacting specificity residues to mutate without catastrophic loss of DNA binding. In contrast, C2H2-ZF DNA binding in fungi, plants, and other lineages is constrained by reliance on base-contacting residues for DNA-binding functionality. Reconstructions indicate that virtually every DNA triplet was recognized by at least one C2H2-ZF domain in the common progenitor of placental mammals, but that extant C2H2-ZF domains typically bind different sequences from these ancestral domains, with changes facilitated by non-base-contacting residues. Conclusions Our results suggest that the evolution of C2H2-ZFs in metazoans was expedited by the interaction of non-base-contacting residues with the DNA backbone. We term this phenomenon “kaleidoscopic evolution,” to reflect the diversity of both binding motifs and binding motif transitions and the facilitation of their diversification.

It is known that changes in temperature may produce stresses in concrete structures of mainly the same order of magnitude as the dead and live loads in some cases. However, the stresses due to temperature are produced only when the thermal expansion or contraction is restrained. In this paper, the behavior of reinforced concrete beams and frames is studied under thermal loads, with the presence of dead and live loads, in order to examine the effect of temperature variation. The beams and frames are modeled properly by accounting for material nonlinearity, particularly cracking. Different temperature gradients, uniform, linear and nonlinear, are considered. The finite element method is employed for conducting the analysis utilizing the computer code ABAQUS. The obtained results of the studied cases reveal that material modeling of reinforced concrete beams and frames plays a major role in how these structures react to temperature variation. Cracking contributes to the release of significant portion of temperature restrain and in some cases this restrain is almost eliminated. The response of beams and frames deviates significantly based on the temperature gradient, linear or nonlinear; hence, the nonlinear temperature gradient which is the realistic profile is important to implement in the analysis.

The CRISPR-associated endonuclease Cas9 from Streptococcus pyogenes (SpyCas9), along with a programmable single-guide RNA (sgRNA), has been exploited as a significant genome-editing tool. Despite the recent advances in determining the SpyCas9 structures and DNA cleavage mechanism, the cleavage-competent conformation of the catalytic HNH nuclease domain of SpyCas9 remains largely elusive and debatable. By integrating computational and experimental approaches, we unveiled and validated the activated Cas9-sgRNA-DNA ternary complex in which the HNH domain is neatly poised for cleaving the target DNA strand. In this catalysis model, the HNH employs the catalytic triad of D839-H840-N863 for cleavage catalysis, rather than previously implicated D839-H840-D861, D837-D839-H840, or D839-H840-D861-N863. Our study contributes critical information to defining the catalytic conformation of the HNH domain and advances the knowledge about the conformational activation underlying Cas9-mediated DNA cleavage.

Fairness, low latency, and high throughput with low energy consumption are desired attributes for medium access control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical layers standard for IPv6 over low power personal area network (6LoWPAN). When a non-appropriate parameter setting is used, the default MAC parameters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies that investigate the impact of optimizing these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to selecting the optimal 6LoWPAN MAC layer parameters set; the introduced mechanism depends on artificial neural networks, genetic algorithm, or particles swarm optimization to select and validate the optimized MAC parameters. The obtained simulation results showed that utilizing the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network.

Due to the rapid development in design of multistory buildings and, because of the variation in stories height with the fixed length of the steel reinforcing bars, steel is usually wasted particularly in columns. Hence, compression lap splice may be used within the column height in order to avoid such waste in steel reinforcement which necessitates the optimization of this splice length. In the ACI 318-11 Provisions, the calculated compression lap splice in members with high strength concrete can be longer than tension splice. Hence, an optimization of compression lap splice would contribute to significant saving particularly in columns. This paper presents an experimental study to investigate the behavior of compression lap splice in circular reinforced concrete columns. The objective of the present investigation is to study the influence of splice length, volume of transverse reinforcement and end bearing condition on the behavior of compression lap splice. The conducted investigation included experimental tests of nine circular columns under uniaxial compression loads. All spliced bars were in contact with each other and with constant concrete cover. Based on the experimental investigation and test results, concluding remarks have been drawn, based on which a design simplified equation for splice length in compression has been developed. A correlation between the experimental and calculated results of the author specimens and other results available in literature, showed a good agreement. Also, the formulas adapted by different codes for predicting the compression lap splice length have been checked with the proposed equation.

In this paper, a system for repairing damaged flat plates as a result of punching shear is presented. The system depends mainly on repairing the damaged concrete and the addition of vertical studs with different arrangements through holes drilled in the plates. The experimental program consists of eight specimens with the same dimensions and flexural reinforcements, but with different concrete strength and shear reinforcement ratios. The main aim of the tests is to assess the efficiency of the suggested repairing system and to investigate the slabs load carrying capacity, deformation characteristics and cracking behavior. Test results showed that using the proposed system on repairing damaged flat plates due to punching shear is very efficient. Theoretical results obtained based on the formulas adopted by different codes and from the critical shear crack theory (CSCT), showed a satisfactory agreement with test results.

Sometimes, due to design mistakes or a change in the construction function results in heavier or additional loads, a flat slab may be failed by punching shear which is considered as a brittle failure. A lot of research work has been carried with the objectives of strengthening existing flat slabs against punching shear. Nevertheless, very few experimental tests have been conducted for the investigation of the behavior of repaired flat slabs damaged due to punching shear. An experimental investigation with the objective of retrofitting flat slabs damaged due to punching shear using prestressed vertical bolts is presented in this paper. The parameters examined in this study are vertical prestressed bolts with different ratios within the slab thickness, slab thickness and central column size. Through the experimental tests the load carrying capacity, deformation characteristics and the cracking behavior have been investigated. A comparison between the behavior of retrofitted slabs and their references showed that the proposed system of repair is effective and could be used in practice. A comparison between the experimental results and calculated punching failure load based on the formulas adopted by different codes, showed a reasonable agreement.

The functions of the majority of trypanosomatid-specific proteins are unknown, hindering our understanding of the biology and pathogenesis of Trypanosomatida. While protein-protein interactions are highly informative about protein function, a global map of protein interactions and complexes is still lacking for these important human parasites. Here, benefiting from in-depth biochemical fractionation, we systematically interrogated the co-complex interactions of more than 3354 protein groups in procyclic life stage of Trypanosoma brucei, the protozoan parasite responsible for human African trypanosomiasis. Using a rigorous methodology, our analysis led to identification of 128 high-confidence complexes encompassing 716 protein groups, including 635 protein groups that lacked experimental annotation. These complexes correlate well with known pathways as well as for proteins co-expressed across the T. brucei life cycle, and provide potential functions for a large number of previously uncharacterized proteins. We validated the functions of several novel proteins associated with the RNA-editing machinery, identifying a candidate potentially involved in the mitochondrial post-transcriptional regulation of T. brucei. Our data provide an unprecedented view of the protein complex map of T. brucei, and serve as a reliable resource for further characterization of trypanosomatid proteins. The presented results in this study are available at: www.TrypsNetDB.org.

BACKGROUND AND OBJECTIVES: The objective of this study was to assess the knowledge, perception, and professional experience of pediatricians in Saudi Arabia regarding child abuse and neglect. DESIGN AND SETTING: Descriptive study during a one day pediatric conference held on King King Abdulaziz University Hospital, a tertiary care teaching hospital in western Saudi Arabia. PATIENTS AND METHODS: The study targeted 198 attendees who were invited from different healthcare sectors in the country. RESULTS: The overall knowledge of participants about some important aspects of child abuse and negligence was adequate, ranging between 82% and 91%. However, their knowledge about reporting cases of child abuse and neglect was quite deficient, ranging between 66% and 79%. As for “professional experience about child abuse and negligence,” it showed considerable variation between participants ranging between 43% and 82%, in which pediatricians who received their medical education in Saudi Arabia scored statistically significantly higher, while pediatricians who received their medical education in Western countries scored higher in all other aspects of the study. CONCLUSIONS: Currently, the knowledge and clinical experience on the subject of child abuse and neglect in Saudi Arabia is enough to adopt a comprehensive strategy for the prevention and interventions of child maltreatment at all levels. Pediatricians are expected to play a key role by leading and facilitating this process.

The original version of this Article contained an error in Figure 3, where panel d was inadvertently replaced with a duplicate of panel c during typesetting. Also, the legend of Figure 5f incorrectly read ‘310 AD patients (blue dots, r = –0.4) and 157 non-demented individuals (green dots, r = –0.1)’, and should have read ‘310 AD patients (blue dots, r = –0.1) and 157 non-demented individuals (green dots, r = –0.4)’. Both of these errors have now been corrected in both the PDF and HTML versions of the Article.

In light of the numerous studies identifying post-transcriptional regulators on the surface of the endoplasmic reticulum (ER), we asked whether there are factors that regulate compartment specific mRNA translation in human cells. Using a proteomic survey of spatially regulated polysome interacting proteins, we identified the glycolytic enzyme Pyruvate Kinase M (PKM) as a cytosolic (i.e. ER-excluded) polysome interactor and investigated how it influences mRNA translation. We discovered that the PKM-polysome interaction is directly regulated by ADP levels-providing a link between carbohydrate metabolism and mRNA translation. By performing enhanced crosslinking immunoprecipitation-sequencing (eCLIP-seq), we found that PKM crosslinks to mRNA sequences that are immediately downstream of regions that encode lysine- and glutamate-enriched tracts. Using ribosome footprint protection sequencing, we found that PKM binding to ribosomes causes translational stalling near lysine and glutamate encoding sequences. Lastly, we observed that PKM recruitment to polysomes is dependent on poly-ADP ribosylation activity (PARylation)-and may depend on co-translational PARylation of lysine and glutamate residues of nascent polypeptide chains. Overall, our study uncovers a novel role for PKM in post-transcriptional gene regulation, linking cellular metabolism and mRNA translation.