bluuebunny/crossref_metadata_2025 huggingface dataset:
Piezo1 regulates cholesterol biosynthesis to influence neural stem cell fate during brain development
Authors: Nourse Jamison L. , Leung Vivian M. , Abuwarda Hamid , Evans Elizabeth L. , Izquierdo-Ortiz Esmeralda , Ly Alan T. , Truong Nguyen , Smith Samantha , Bhavsar Harsh , Bertaccini Gabriella , Monuki Edwin S. , Panicker Mitradas M. , Pathak Medha M.
Published: September 2022

Mechanical forces and tissue mechanics influence the morphology of the developing brain, but the underlying molecular mechanisms have been elusive. Here, we examine the role of mechanotransduction in brain development by focusing on Piezo1, a mechanically activated ion channel. We find that Piezo1 deletion results in a thinner neuroepithelial layer, disrupts pseudostratification, and reduces neurogenesis in E10.5 mouse embryos. Proliferation and differentiation of Piezo1 knockout (KO) mouse neural stem cells (NSCs) isolated from E10.5 embryos are reduced in vitro compared to littermate WT NSCs. Transcriptome analysis of E10.5 Piezo1 KO brains reveals downregulation of the cholesterol biosynthesis superpathway, in which 16 genes, including Hmgcr, the gene encoding the rate-limiting enzyme of the cholesterol biosynthesis pathway, are downregulated by 1.5-fold or more. Consistent with this finding, membrane lipid composition is altered, and the cholesterol levels are reduced in Piezo1 KO NSCs. Cholesterol supplementation of Piezo1 KO NSCs partially rescues the phenotype in vitro. These findings demonstrate a role for Piezo1 in the neurodevelopmental process that modulates the quantity, quality, and organization of cells by influencing cellular cholesterol metabolism. Our study establishes a direct link in NSCs between PIEZO1, intracellular cholesterol levels, and neural development.
A New Position Calibration Method for MUSER Images
Authors: Zhou Zhichao , Yan Yihua , Chen Linjie , Wang Wei , Ma Suli
Published: September 2022

The Mingantu Spectral Radioheliograph (MUSER), a new generation of solar dedicated radio imaging-spectroscopic telescope, has realized high-time, high-angular, and high-frequency resolution imaging of the Sun over an ultra-broadband frequency range. Each pair of MUSER antennas measures the complex visibility in the aperture plane for each integration time and frequency channel. The corresponding radio image for each integration time and frequency channel is then obtained by inverse Fourier transformation of the visibility data. However, the phase of the complex visibility is severely corrupted by instrumental and propagation effects. Therefore, robust calibration procedures are vital in order to obtain high-fidelity radio images. While there are many calibration techniques available—e.g., using redundant baselines, observing standard cosmic sources, or fitting the solar disk—to correct the visibility data for the above-mentioned phase errors, MUSER is configured with non-redundant baselines and the solar disk structure cannot always be exploited. Therefore it is desirable to develop alternative calibration methods in addition to these available techniques whenever appropriate for MUSER to obtain reliable radio images. In the case where a point-like calibration source contains an unknown position error, we have for the first time derived a mathematical model to describe the problem and proposed an optimization method to calibrate this unknown error by studying the offset of the positions of radio images over a certain period of the time interval. Simulation experiments and actual observational data analyses indicate that this method is valid and feasible. For MUSER’s practical data the calibrated position errors are within the spatial angular resolution of the instrument. This calibration method can also be used in other situations for radio aperture synthesis observations.
Novel Micro flow Sensor for Air Purge Method to Monitor the State of Charge of Lead Acid Battery
Authors: Adhav Yashwant , Sonawane Dayaram , Patil Chetankumar
Published: September 2022

The foremost aim of the present research study is to measure specific gravity of lead-acid batteries and further know batterys state-of-charge (SoC). In this article, air purge method is used to monitor the status of a battery; back differential pressure is measured using MPX4006 DP MEMS pressure sensor. In air purge method, it is essential to adjust the rate of bubbles in acid. This can be achieved by controlling the compressors speed or controlling the air flow by controllers. When air purge tubes are inserted through a batterys cap, it is difficult to observe bubbles produced in the acid as the battery is nontransparent. If the bubble rate is too slow, the system lowers the output signal and hence stops working. During the calibration of the instrument, air flow measurement through the pipe is of prime importance. In this method, FS1012 MEMS gas flow sensor is used to monitor the discharged air flow. FS1012 is based on the thermo-transfer principle. Compared to the resistive technique, this flow sensor consists of a thermopile for air flow sensing whose signal-noise ratio is exceptional. The output of the sensor is exponential; signal conditioning circuit has been designed to get linear output 0 to 3.3 V standard range. The amplified output of signal conditioning circuit is monitored using TM4C123GXL arm controller. FS1020 is first time proposed to monitor the air flow of a small air purge probe used to monitor the state of charge of a lead-acid battery. FS1020 ensures an ideal flow of the air bubbler system, which improves the performance of the battery charge monitoring system. Conventional flow sensors for example, rotameter cannot be used for very low flow measurement; they are costlier as well as bulky. The minimum flow required to produce sufficient bubbles in acid is 0.50 liter per minute (LPM) which is measured by FS 1012.
Investigation of increasing non-linear load impact on power quality in the distribution system
Authors: Maury Navin , Singh Amit Kumar
Published: September 2022

Now a day’s power quality problem daily increases due to the use of non-linear load in all sectors. So in this paper, we work on power quality improvement help of DVR (dynamic voltage restorer). Power quality issues are present time most thinkful because all sectors use power electronics devices and automation (PLC). In any sector without the use of power electronics devices and non-linear load create more problems because now a day all sectors use embedded and automation systems. The power quality main issue are sag, swells, unbalance, fault, and harmonic in this paper we control and reduce the sag in a fixed distribution system with the help of DVR with a PID controller. PID controller connected in series with DVR which is the controller the voltage sag and its frequency. This combination improves the power quality in the distribution system we can see the improvement by a simulation result shown in the paper we use 11kv subsystem and three-phase step-down transformer which converts 11kv to 440v. This project improves the power quality issue on the distribution side.
Detection of Security Attacks on Edge Computing of IoT Devices through NS2 Simulation
Authors: Kumar Anit , Singh Dhanpratap
Published: September 2022

The limited resource and computation capacity of an IoT device always being a target vector for intruders to use to steal sensitive data from the device. Computational problems later solved by the engineers by introducing an edge server near to IoT device network boundary, but still it is not secure from the hands of intruders. IoT device vulnerabilities still can allow Edge server to give the control of the device to the intruders. Hence both the Edge server and the connected IoT device may fall under the control of an intruder for a misuse. In this paper we study how IoT devices in case of Edge computing falls under security threats. We have used NS2 simulator to identify a malicious node that behaves inconsistent in terms of Packet Delivery Ratio, End-to-End Delay, and Throughput.
Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective
Authors: Ukani Neema Amish , Chakole Saurabh S.
Published: September 2022

Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.
Improve Content-based Image Retrieval using Deep learning model
Authors: Kumar Suneel , Singh Manoj Kumar , Mishra Manoj Kumar
Published: September 2022

The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
Efficient Cluster Head Selection and Trust Based Routing in MANET
Authors: Bharti Meena , Rani Shaveta , Singh Paramjeet
Published: September 2022

Mobile Adhoc Network (MANET) faces different security challenges due to its collaborative and open communication behavior and restricted availability of resources. Routing is hard and vulnerabilities issues are regularly exposed to highly dynamic nature and numerous communication breakdowns induced by mobile nodes. Thus, security measures that can minimize the effects of various attacks must be included in the MANET’s routing protocols. Secure communication with optimal energy is a big challenge in such networks. This paper presents a secure optimal routing scheme. The objective of the scheme is to find the most optimal secure path with low energy consumption using the proposed function. This scheme is divided into two algorithms; the first algorithm finds cluster head effectively and the second computes the trust value of each path for secure communication. The trust value is computed using the trust-based scheme using packet forwarding information. On the basis of the trust value, this algorithm detects the malicious nodes and removes them from the network and then the other proposed routing scheme computes the optimal path. Then path with less hop count and minimum energy usage is selected optimally.
Classification of Sleep using Polysomnography
Authors: Farooq Hemu , Jain Anuj , Shukla M.K.
Published: September 2022

Sleep is often recognized as a necessary component of a person’s well-being and is an extremely vital component of a healthy person’s well-being. Sleep is a state in which a person is both physiologically and psychically at ease. The sleep conception is appraised exceedingly unusual, and it has piqued the interest of researchers all around the world. The stages of sleep are examined in order give benefits for studying sleep utilized for the purpose of research. The ability to diagnose sleep disorders has been demonstrated by carefully examining the sleep score system and its various stages. As can be seen, manual sleep stage classification is a time-consuming method that requires adequate measure for sleep professionals to undertake statistical and quantitative analysis. Furthermore, errors and abnormalities in the categoization of the similar facts can occur frequently. As a result, the adoption of an autonomous scoring system to enable trustworthy classification is becoming increasingly popular. The scheduled task teaches you how to apply an automatic system based on EEG (Electroencephalogram), EMG (Electromyogram) and EOG (Electrooculogram), which is known as a polysomnography test or PSG test. For a total of 30 seconds, the recording was measured in length segments. The standard collection of parameters, which gives the distinct stages of sleep, was obtained from several hospitals in sleep laboratories. Sleep waking, Non-Rapid Eye Movement (Stage 1, Stage2, Stage3), and Rapid Eye Movement are the stages. In clinics, the procedure can be highly effective, especially for neurologist detecting sleep problems.
Machine learning based design of reinforced concrete shear walls subjected to earthquakes
Authors: Rao Praveena , Pollayi Hemaraju , Rao Madhuri
Published: September 2022

Civil engineering structural components are classified according to their projected structural performance in the present building code regulations and design standards. These building design codes are largely based upon previous experimental results of thousands of samples tested to failure and validated with analytical solutions. Machine Learning techniques (ML) is a subset of Artificial Intelligence (AI) that facilitates classification and prediction of structural performances for a broad spectrum of complex structures with greater accuracy. Machine learning models have the potential to make reliable predictions with the help of algorithms. Thereby, saving a tremendous amount of time and resources invested in experimental investigations of large structural components such as shear walls and columns. The ML algorithms can learn from the available data, deduce underlying inter-relationships, make inferences and detect patterns based on previous experience. In the present work, various ML algorithms were implemented to identify the influence of geometrical as well as mechanical characteristics. Database of 393 specimens of reinforced concrete shear walls with rectangular (R), flanged (F) and barbell (B) cross-sections are adopted for the analysis. Shear walls are fundamentally classified into four failure categories which include flexure or due to bending, shear, intermediate flexure-shear and sliding due to shear. The objective of this paper is to classify and predict the shear strength, flexural strength as per the Indian standard code provisions and failure modes of shear walls with the help of ML techniques. Algorithms such as KNearest Neighbors, Naive Bayes, Decision Tree, Random Forest, AdaBoost, LightGBM, XGBoost and Cat-Boost is implemented using Python. Highest accuracy of 85% is achieved on the test set by Random Forest, 83% by CatBoost and 81% by LightGBM boosting algorithms. It is observed that input variables such as aspect ratio (lw/tw), characteristic strength of concrete in compression (f

ck
), characteristic yield strength of steel (f

y
), percentage of steel (ρ), web vertical reinforcement, horizontal reinforcement, boundary element reinforcements play a vital role in governing the shear strength (V

u
) and flexural strength (M

u
) of shear walls.
Privacy Preservation of Digital Health Records in Cloud
Authors: Munjal Kundan , Bhatia Rekha
Published: September 2022

The variety and amount of patient healthcare digital data is rapidly expanding. The field of artificial intelligence has fast proven revolutionary for healthcare, allowing for unprecedented speed and precision in data analysis. Many hospitals have already transitioned to electronic health records (EHRs), a digital version of paper medical information. Information Technology has already aided in simplifying operations in this area, making the process far more efficient and patient-centered than in the past. A patient’s trust may disintegrate in the face of recurrent incidents if there is no precise control and safety in place. As a result, it limits the potential for digital health to promote an age of more accessible, connected, and individualized treatment. When it comes to picking a cloud solution, healthcare providers are most concerned about security and privacy. In this research, we seek to demonstrate various ways that aid patient data privacy while simultaneously allowing cloud services to be used.
Fatigue Detection Post Physical Activity Using Machine Learning Algorithms
Authors: Sharma Ashish , Sethi Gaurav
Published: September 2022

Construction work is purely effortful and the prevention of injuries at construction job sites is essential for encouraging worker’s well being and health which is generally overlooked at the construction sites. World’s construction industry is one amongst those having unsatisfactory work health issues. A large number of laborers and construction workers have to undergo fatigue risk at their job place. This paper describes the current state of the art of the research carried out in case of fatigue assessment after performing some physical activity providing an insight into fatigue, its detection and an overview of the causes of risk fatigue and its countermeasures. A number of subjective and objective fatigue assessment approaches have been used that have further stimulated the inclusion of latest and advanced approaches for fatigue detection. Although individual’s knowledge regarding the fatigue detection approaches has been enhanced, there is as yet minimal research in the field of fatigue detection post physical activity. In this study the stress would be on objective fatigue detection techniques where the acquisition of physiological data of construction workers is required. The study provides a ground for detection of physiological fatigue based on motion capture data in the form of videos.
A Review on the Detection of the Post COVID-19 Symptoms for Long Term Diseased Patients using Machine Learning Algorithms
Authors: Patibandla Anitha
Published: September 2022

Long term diseases require continuous monitoring, sometimes periodic monitoring to verify if any serious concern requires an attention. In recent years, it is noticed that the COVID-19 pandemic has triggered serious concern towards the long-term diseased individuals. As the mortality rate of the COVID-19 clearly indicates that the highest percentage of deaths reflect in the individuals suffering from long term diseases such as diabetes, pneumonia, cardiovascular and acute renal failure. Though they are tested for COVID negative through conventional apparatus, it doesn’t confer that they are completely out of post consequences. Hence a periodic, if necessary continuous monitoring needs to be aided, which in current scenario is a challenging task. Hence, our current article reviews the use of machine learning algorithms to detect and diagnose pre and post COVID-19 effects on long term diseased patients.
A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and Techniques
Authors: Kumar Davinder , Ganesh Aman
Published: September 2022

Hand gesture recognition systems are gaining popularity these days due to the ease with which humans and machines can communicate. The goal of hand gesture development is to improve interactions between humans and computers for the purpose of transmitting ideas. In a typical HGR systems, the main steps followed are, data collection, pre-processing, feature extraction and classification. For every stage, a significant number of techniques are available with various other sub steps. This study gives an overview of modern hand gesture recognition techniques, its Physiological and Anatomical Background, working and challenges faced by these systems. Moreover, the role of artificial intelligence in optimizing the performance of HGR systems is also delineated in this paper. Also, the precision and accuracy of the HGR approaches gets affected by the complexity and diversity of various hand movements, therefore, the need for implementing AI based ML and DL methods keeps on rising. Keeping this in mind, the performance of various ML algorithms in recognizing the visual and sensor-based hand gestures is investigated. Moreover, the commonly utilized framework in detecting hand gestures has been explored in numerous standard datasets.
Past, Present and Future of Automated Mammographic Density Measurement for Breast Cancer Risk Prediction
Authors: Pawar Shivaji D. , Joshi Pratibha T. , Savalkar Vishwayogita A. , Sharma Kamal Kr. , Sapate Suhas G.
Published: September 2022

Mammography is one of the essential screening technologies which is helpful to save the lives of women against breast cancer. Prediction of breast cancer from mammograms is not reached on its optimal level; hence there is a constant enhancement in clinical applications for mammographic breast density measurement. Optimal results in breast density measurement can be helpful to provide better care for women who have dense breasts. The sensitivity of digital mammograms reduces significantly in case dense breast, which may lead further to hide the cancerous lesions and may be converted into high stage breast cancer. Many research innovations and clinical applications are developed to support radiologists for the second opinion and predict breast cancer risk in advance. But still, there is an unsolved research question: which one is “dense breast” and which screening modularity is suitable for the dense breast to avoid the risk of breast cancer. Hence, currently, radiologists measure mammographic breast density with the help of BI-RADS classification, which is subjective. The basic inclination of this paper is to provide an overview of different clinical applications used for breast density measurement in the past and present with their potential technologies, advantages, disadvantages and current status of all various clinical applications in the market and provides thoughts on the future of automated mammographic breast density measurement.
Multi-stakeholder analysis of fire risk reduction in a densely populated area in the Netherlands: a case-study in the Veluwe area
Authors: de Hoop Evelien , Brouwers Hilde J H , Buijs Sophie L , Egberts Linde , van Gerrevink Max J , de Ruiter Marleen C , Veraverbeke Sander
Published: September 2022

Reducing the climate change-induced risk of uncontrollable fires in landscapes under nature management, with severe impacts on landscape and society, is particularly urgent in densely-populated and fragmented areas. Reducing fire risk in such areas requires active involvement of a wide diversity of stakeholders. This research letter investigates stakeholders’ needs with regard to fire risk reduction in the Veluwe area in the Netherlands. This densely populated landscape is a popular tourist attraction, and it is one of the most fire-prone landscapes of the Netherlands, with abundant fuels and human ignition sources. We draw upon seven in-depth qualitative interviews with key stakeholders in the Veluwe area, which we situate in a wider review of existing literature. Our analysis demonstrates that the rising incidence of uncontrollable fires poses four types of new challenges to these stakeholders in the Veluwe area. First, stakeholders express the need to reshape existing policy tools and develop novel ones that create synergies between existing policy-priorities (e.g. biodiversity conservation) and fire risk reduction. Second, stakeholders argue for a critical rethinking of the value of landscapes in society, and the diverse roles that fire may play in landscape management research and practice. Third, developing such policy tools requires new modalities and platforms for multi-stakeholder and multi-level collaboration, which are currently lacking because the current and expected future risk of uncontrollable fire is unprecedented. And fourth, the development of effective policy tools requires new knowledge that is interdisciplinary, sensitive towards the local social and ecological characteristics of the area, and which approaches current fire risk challenges and their possible solutions dynamically. While our stakeholder analysis is specific to the Veluwe area in the Netherlands, our findings are also likely to be relevant to other fire-prone nature areas in fragmented landscapes, particularly in Northwestern Europe.
Interactions between hot and dry fuel conditions and vegetation dynamics in the 2017 fire season in Portugal
Authors: Ermitão Tiago , Gouveia Célia M , Bastos Ana , Russo Ana C
Published: September 2022

Wildfires are a serious threat to ecosystems and human. In Portugal, during 2017, a catastrophic fire season burned more than 500 000 hectares and caused the death of more than 100 people. Previous studies have shown that hot and dry fuel conditions promoted widespread propagation of wildfires. However, burned area (BA) and mega-fires, such as the 2017 ones, depend not just on favourable meteorological conditions, but also on fuel accumulation and dryness. In this study, we will assess the influence of spring meteorological conditions on fire season BA, through their effects on fuel accumulation and dryness. Using satellite-based data, we show that the association of higher temperatures and water availability in spring can increase the risk of summer wildfires propagation, flammability and intensity through their influence on vegetation gross productivity. This study highlights the important role of fuel accumulation during the growing season in fire-prone regions like Portugal. Our results imply that fuel management may be an effective way to mitigate extreme fire seasons associated with warmer and drier conditions in the future.
Exact solution of a family of staggered Heisenberg chains with conclusive pretty good quantum state transfer
Authors: Serra Pablo , Ferrón Alejandro , Osenda Omar
Published: September 2022

We construct the exact solution for a family of one-half spin chains explicitly. The spin chains Hamiltonian corresponds to an isotropic Heisenberg Hamiltonian, with staggered exchange couplings (ECs) that take only two different values. We work out the exact solutions in the one-excitation subspace. Regarding the problem of quantum state transfer, we use the solution and some theorems concerning the approximation of irrational numbers, to show the appearance of conclusive pretty good transmission for chains with particular lengths. We present numerical evidence that pretty good transmission is achieved by chains whose length is not a power of two. The set of spin chains that shows pretty good transmission is a subset of the family with an exact solution. Using perturbation theory, we thoroughly analyze the case when one of the EC strengths is orders of magnitude larger than the other. This strong coupling limit (SCL) allows us to study, in a simple way, the appearance of pretty good transmission. The use of analytical closed expressions for the eigenvalues, eigenvectors, and transmission probabilities allows us to obtain the precise asymptotic behavior of the time where the pretty good transmission is observed. Moreover, we show that this time scales as a power law whose exponent is an increasing function of the chain length. We also discuss the crossover behavior obtained for the pretty good transmission time between the regimes of SCL and the one observed when the ECs are of the same order of magnitude.
Core–shell bioprinting of vascularized in vitro liver sinusoid models
Authors: Taymour Rania , Chicaiza-Cabezas Nathaly Alejandra , Gelinsky Michael , Lode Anja
Published: September 2022

In vitro liver models allow the investigation of the cell behavior in disease conditions or in response to changes in the microenvironment. A major challenge in liver tissue engineering is to mimic the tissue-level complexity: besides the selection of suitable biomaterial(s) replacing the extracellular matrix (ECM) and cell sources, the three-dimensional (3D) microarchitecture defined by the fabrication method is a critical factor to achieve functional constructs. In this study, coaxial extrusion-based 3D bioprinting has been applied to develop a liver sinusoid-like model that consists of a core compartment containing pre-vascular structures and a shell compartment containing hepatocytes. The shell ink was composed of alginate and methylcellulose (algMC), dissolved in human fresh frozen plasma. The algMC blend conferred high printing fidelity and stability to the core–shell constructs and the plasma as biologically active component enhanced viability and supported cluster formation and biomarker expression of HepG2 embedded in the shell. For the core, a natural ECM-like ink based on angiogenesis-supporting collagen-fibrin (CF) matrices was developed; the addition of gelatin (G) enabled 3D printing in combination with the plasma-algMC shell ink. Human endothelial cells, laden in the CFG core ink together with human fibroblasts as supportive cells, formed a pre-vascular network in the core in the absence and presence of HepG2 in the shell. The cellular interactions occurring in the triple culture model enhanced the albumin secretion. In conclusion, core–shell bioprinting was shown to be a valuable tool to study cell–cell-interactions and to develop complex tissue-like models.
Rural electrification in Africa: a case study of Yebu community solar minigrid
Authors: Edomah Norbert
Published: September 2022

Using field observation and focus group semi-structured interviews, we explored rural electrification dynamics within a rural community setting. We investigated the issues surrounding the emergence, deployment, operation and management of solar minigrid technology within Yebu community in the Federal Capital Territory, north-central Nigeria, and the associated issues that emerged over time. The study revealed that: (1) many solar minigrid energy access interventions do not have a viable business and sustainability plan that guarantees long term impact; (2) a lack of participatory decision making by all concerned stakeholders (from the project conception stage) has hindered the potential of many solar minigrid energy access projects to achieve their intended developmental and economic benefits. The paper concludes by highlighting the key recommendations that can help address these challenges.
Multiple similarity drug–target interaction prediction with random walks and matrix factorization
Authors: Liu Bin , Papadopoulos Dimitrios , Malliaros Fragkiskos D , Tsoumakas Grigorios , Papadopoulos Apostolos N
Published: September 2022

The discovery of drug–target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC), respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.
Not all T cell epitopes are equally desired: a review ofin silicotools for the prediction of cytokine-inducing potential of T-cell epitopes
Authors: Dhanda Sandeep Kumar , Malviya Jitendra , Gupta Sudheer
Published: September 2022

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.
A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction
Authors: Wang Lei , Wong Leon , Li Zhengwei , Huang Yuan , Su Xiaorui , Zhao Bowei , You Zhuhong
Published: September 2022

Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have been designed to predict circRNA-disease associations. However, most of the existing methods rely on single correlation data. Here, we propose a machine learning framework for circRNA-disease association prediction, called MLCDA, which effectively fuses multiple sources of heterogeneous information including circRNA sequences and disease ontology. Comprehensive evaluation in the gold standard dataset showed that MLCDA can successfully capture the complex relationships between circRNAs and diseases and accurately predict their potential associations. In addition, the results of case studies on real data show that MLCDA significantly outperforms other existing methods. MLCDA can serve as a useful tool for circRNA-disease association prediction, providing mechanistic insights for disease research and thus facilitating the progress of disease treatment.
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism
Authors: Zhang Zhao-Yue , Ning Lin , Ye Xiucai , Yang Yu-He , Futamura Yasunori , Sakurai Tetsuya , Lin Hao
Published: September 2022

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).
SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising
Authors: Liu Jian , Pan Yichen , Ruan Zhihan , Guo Jun
Published: September 2022

Single-cell sequencing technologies are widely used to discover the evolutionary relationships and the differences in cells. Since dropout events may frustrate the analysis, many imputation approaches for single-cell RNA-seq data have appeared in previous attempts. However, previous imputation attempts usually suffer from the over-smooth problem, which may bring limited improvement or negative effect for the downstream analysis of single-cell RNA-seq data. To solve this difficulty, we propose a novel two-stage diffusion-denoising method called SCDD for large-scale single-cell RNA-seq imputation in this paper. We introduce the diffusion i.e. a direct imputation strategy using the expression of similar cells for potential dropout sites, to perform the initial imputation at first. After the diffusion, a joint model integrated with graph convolutional neural network and contractive autoencoder is developed to generate superposition states of similar cells, from which we restore the original states and remove the noise introduced by the diffusion. The final experimental results indicate that SCDD could effectively suppress the over-smooth problem and remarkably improve the effect of single-cell RNA-seq downstream analysis, including clustering and trajectory analysis.
Fluid management in acute kidney injury: from evaluating fluid responsiveness towards assessment of fluid tolerance
Authors: Argaiz Eduardo R , Rola Philippe , Haycock Korbin H , Verbrugge Frederik H
Published: September 2022

Despite the widespread use of intravenous fluids in acute kidney injury (AKI), solid evidence is lacking. Intravenous fluids mainly improve AKI due to true hypovolaemia, which is difficult to discern at the bedside unless it is very pronounced. Empiric fluid resuscitation triggered only by elevated serum creatinine levels or oliguria is frequently misguided, especially in the presence of fluid intolerance syndromes such as increased extravascular lung water, capillary leak, intra-abdominal hypertension, and systemic venous congestion. While fluid responsiveness tests clearly identify patients who will not benefit from fluid administration (i.e. those without an increase in cardiac output), the presence of fluid responsiveness does not guarantee that fluid therapy is indicated or even safe. This review calls for more attention to the concept of fluid tolerance, incorporating it into a practical algorithm with systematic venous Doppler ultrasonography assessment to use at the bedside, thereby lowering the risk of detrimental kidney congestion in AKI.
Pericoronary fat attenuation index—a new imaging biomarker and its diagnostic and prognostic utility: a systematic review and meta-analysis
Authors: Sagris Marios , Antonopoulos Alexios S , Simantiris Spiridon , Oikonomou Evangelos , Siasos Gerasimos , Tsioufis Konstantinos , Tousoulis Dimitris
Published: September 2022

Pericoronary fat attenuation index (FAI) on coronary computed tomography angiography imaging has been proposed as a novel marker of coronary vascular inflammation with prognostic value for major cardiovascular events. To date, there is no systematic review of the published literature and no meta-analysed data of previously published results. We performed a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. We systematically explored published literature in MEDLINE (PubMed) before 20 January 2022 for studies assessing FAI in both diagnostic and prognostic clinical settings in patients with or without cardiovascular disease. The primary outcome was the mean difference in FAI attenuation between stable and unstable coronary plaques. The secondary outcome was the hazard ratio (HR) of high FAI values for future cardiovascular events. We calculated I2 to test heterogeneity. We used random-effects modelling for the meta-analyses to assess the primary and secondary outcomes. This study is registered with PROSPERO (CRD42021229491). In total, 20 studies referred in a total of 7797 patients were included in this systematic review, while nine studies were used for the meta-analysis. FAI was significantly higher in unstable compared with stable plaques with a mean difference of 4.50 Hounsfield units [95% confidence interval (CI): 1.10–7.89, I2 = 88%] among 902 patients. Higher pericoronary FAI values offered incremental prognostic value for major adverse cardiovascular events (MACEs) in studies with prospective follow-up (HR = 3.29, 95% CI: 1.88–5.76, I2 = 75%) among 6335 patients. Pericoronary FAI seems to be a promising imaging biomarker that can be used for the detection of coronary inflammation, possibly to discriminate between stable and unstable plaques, and inform on the prognosis for future MACE. Further validation of these findings and exploration of the cost-effectiveness of the method before implementation in clinical practice are needed.
Atrial fibrillation and chronic obstructive pulmonary disease: diagnostic sequence and mortality risk
Authors: Warming Peder E , Garcia Rodrigue , Hansen Carl J , Simons Sami O , Torp-Pedersen Christian , Linz Dominik , Tfelt-Hansen Jacob
Published: September 2022

Background and aims

Chronic obstructive pulmonary disease (COPD) is present in 13% of atrial fibrillation (AF) patients. In patients diagnosed with both AF and COPD, we aimed to assess overall mortality risk and its association with temporal sequence in AF and COPD diagnosis.

Methods

This nationwide study assessed all patients aged 18–85 years diagnosed with both COPD and AF between 1999 and 2018 in Denmark. Three groups were defined according to the temporal sequence of diagnosis: COPD diagnosed at least 6 months before AF (COPD-First), AF diagnosed at least 6 months before COPD (AF-First) and COPD, and AF diagnosed within a 6-months’ time frame (AF∼COPD).

Results

We included 62 806 patients (75.0 years; 56.5% males). After 5 years of follow-up, 31 494 (50.1%) died. Mortality was highest in the COPD-First group (COPD-First: 52.8%; AF-First: 46.0%; AF∼COPD 50.6%). In a multivariable Cox-regression model adjusted for age, sex, type 2 diabetes, history of acute myocardial infarction, hypertension, heart failure, dyslipidemia, cancer, chronic kidney disease, and stroke, the AF∼COPD group (HR 1.19, 95% CI 1.16–1.23; P < 0.001) and COPD-First group (HR 1.30, 95% CI 1.27–1.33; P < 0.001) had a higher risk of death compared with the AF-First group. A restricted cubic spline analysis showed that the earlier the COPD was diagnosed, the worse is the prognosis.

Conclusion

Patients with concomitant AF and COPD had a very poor prognosis and the temporal sequence in diagnosis was differentially associated with prognosis, where a COPD diagnosis preceding an AF diagnosis was accompanied with a higher mortality risk compared with a COPD diagnosis following an AF diagnosis.
Retroaortic left renal vein in open thoracoabdominal aneurysm repair: A modified approach
Authors: Idhrees Mohammed , Velayudhan Bashi
Published: September 2022

The incidence of retroaortic left renal vein (RLRV) is less than 6%. This anatomical variation hinders the exposure and anastomosis of visceral arteries during open thoracoabdominal aneurysm repair. This situation may warrant division and ligation of the RLRV using the conventional retroperitoneal approach. This report describes a modified approach wherein the vein is not divided, thereby improving its surgical exposure.
Modified biatrial heart transplantation after bilateral bidirectional cavo-pulmonary shunts
Authors: Yoneyama Fumiya , Imamura Michiaki
Published: September 2022

We describe a patient who underwent modified biatrial anastomosis heart transplantation after the bilateral Glenn procedure. We introduced a new surgical technique to use the native central pulmonary artery as systemic venous return, which was anastomosed to right atrium, and then, biatrial anastomosis was performed.
Exploratory action research: experiences of Nepalese EFL teachers
Authors: Shrestha Sagun , Laudari Suman , Gnawali Laxman
Published: September 2022

The number of reports and publications about exploratory action research (EAR) has been increasing in recent years; however, there is still a dearth of studies that examine its effectiveness for improving teaching and learning. This study explores Nepalese EFL teachers’ perceptions of EAR, the ways they explored their classroom issues, and how they acted to improve their classroom practices through involvement in an online EAR training course for a year. The data collected from interviews and the teachers’ written reports show that these teachers found EAR to be an effective approach to explore their classroom puzzles or problems and develop informed, context-sensitive strategies to solve these. The findings of this study are relevant for teachers, teacher educators, and education policymakers in providing insights into the value of EAR for bringing about changes in teaching and learning situations.