Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat cultivars and hybrids at different growth stages.

BACKGROUND: Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. METHODS: In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages. RESULTS: Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles. CONCLUSION: These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat.

Design, Analysis, and Development of Low-Cost State-of-the-Art Magnetorheological-Based Microprocessor Prosthetic Knee.

For amputees, amputation is a devastating experience. Transfemoral amputees require an artificial lower limb prosthesis as a replacement for regaining their gait functions after amputation. Microprocessor-based transfemoral prosthesis has gained significant importance in the last two decades for the rehabilitation of lower limb amputees by assisting them in performing activities of daily living. Commercially available microprocessor-based knee joints have the needed features but are costly, making them beyond the reach of most amputees. The excessive cost of these devices can be attributed to custom sensing and actuating mechanisms, which require significant development cost, making them beyond the reach of most amputees. This research contributes to developing a cost-effective microprocessor-based transfemoral prosthesis by integrating off-the-shelf sensing and actuating mechanisms. Accordingly, a three-level control architecture consisting of top, middle, and low-level controllers was developed for the proposed prosthesis. The top-level controller is responsible for identifying the amputee intent and mode of activity. The mid-level controller determines distinct phases in the activity mode, and the low-level controller was designed to modulate the damping across distinct phases. The developed prosthesis was evaluated on unilateral transfemoral amputees. Since off-the-shelf sensors and actuators are used in i-Inspire, various trials were conducted to evaluate the repeatability of the sensory data. Accordingly, the mean coefficients of correlation for knee angle, force, and inclination were computed at slow and medium walking speeds. The obtained values were, respectively, 0.982 and 0.946 for knee angle, 0.942 and 0.928 for knee force, and 0.825 and 0.758 for knee inclination. These results confirmed that the data are highly correlated with minimum covariance. Accordingly, the sensors provide reliable and repeatable data to the controller for mode detection and intent recognition. Furthermore, the knee angles at self-selected walking speeds were recorded, and it was observed that the i-Inspire Knee maintains a maximum flexion angle between 50° and 60°, which is in accordance with state-of-the-art microprocessor-based transfemoral prosthesis.

Electric Potential at the Interface of Membraneless Organelles Gauged by Graphene.

Eukaryotic cells contain membrane-bound and membrane-less organelles that are often in contact with each other. How the interface properties of membrane-less organelles regulate their interactions with membranes remains challenging to assess. Here, we employ graphene-based sensors to investigate the electrostatic properties of synapsin 1, a major synaptic phosphoprotein, either in a single phase or after undergoing phase separation to form synapsin condensates. Using these graphene-based sensors, we discover that synapsin condensates generate strong electrical responses that are otherwise absent when synapsin is present as a single phase. By introducing atomically thin dielectric barriers, we show that the electrical response originates in an electric double layer whose formation governs the interaction between synapsin condensates and graphene. Our data indicate that the interface properties of the same protein are substantially different when the protein is in a single phase versus within a biomolecular condensate, unraveling that condensates can harbor ion potential differences at their interface.

Effects of Cucumis melo and Citrullus lanatus extracts on glucose level, lipid profile and hepato-renal performance of streptozotocin-induced diabetic albino rats.

Diabetes mellitus, recognized by elevated glucose level in the body fluids is commonly caused by less insulin production or its action. To overcome the complications of diabetes, chemical drugs are never preferred over herbal medicines. Present study was designed to find out the anti-diabetic and health-promoting effects of ethanolic leaf extracts of Cucumis melo and Citrullus lanatus in induced-diabetic albino rats. Thirty male albino rats were bought from the animal house of the university and divided randomly into five feeding groups (n=6). Diabetes was induced in rats of groups A, B, C & D by a single dose of intra-peritoneal injection of streptozotocin (55 mg/Kg), whereas, the rats of group E were considered as control. The rats of groups A, B & C were fed basal diet supplemented with plant extracts (150mg/Kg body weight), whereas; only basal diet was offered to rats of groups D & E. After 28 days of the experiment, blood was collected for biochemical analysis. Results revealed that body weight, glucose, AST, ALB, GGT, HDL, cholesterol, triglyceride, urea and creatinine level differed significantly among treatment groups. It was therefore concluded that ethanolic leaf extracts of Cucumis melo and Citrullus lanatus can be used separately or in combination for the management of diabetes.

Hierarchical Nanospheres with Polycrystalline Ir&Cu and Amorphous Cu2 O toward Energy-Efficient Nitrate Electrolysis to Ammonia.

Electrochemical reduction reaction of nitrate (NITRR) provides a sustainable route toward the green synthesis of ammonia. Nevertheless, it remains challenging to achieve high-performance electrocatalysts for NITRR especially at low overpotentials. In this work, hierarchical nanospheres consisting of polycrystalline Iridium&copper (Ir&Cu) and amorphous Cu2 O (Cux Iry Oz NS) have been fabricated. The optimal species Cu0.86 Ir0.14 Oz delivers excellent catalytic performance with a desirable NH3 yield rate (YR) up to 0.423 mmol h-1  cm-2 (or 4.8 mg h-1  mgcat -1 ) and a high NH3 Faradaic efficiency (FE) over 90% at a low overpotential of 0.69 V (or 0 VRHE ), where hydrogen evolution reaction (HER) is almost negligible. The electrolyzer toward NITRR and hydrazine oxidation (HzOR) is constructed for the first time with an electrode pair of Cu0.86 Ir0.14 Oz //Cu0.86 Ir0.14 Oz , yielding a high energy efficiency (EE) up to 87%. Density functional theory (DFT) calculations demonstrate that the dispersed Ir atom provides active site that not only promotes the NO3 - adsorption but also modulates the H adsorption/desorption to facilitate the proton supply for the hydrogenation of *N, hence boosting the NITRR. This work thus points to the importance of both morphological/structural and compositional engineering for achieving the highly efficient catalysts toward NITRR.

A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals.

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

Performance Analysis of IRS-Assisted THz Communication Systems over α-µ Fading Channels with Pointing Errors.

In this paper, we analyze the performance of an intelligent reflecting surface (IRS)-aided terahertz (THz) wireless communication system with pointing errors. Specifically, we derive closed-form analytical expressions for the upper bounded ergodic capacity and approximate expression of the outage probability. We adopt an α-µ fading channel model for our analysis that is experimentally demonstrated to be a good fit for THz small-scale fading statistics, especially in indoor communication scenarios. In the proposed analysis, the statistical distribution of the α-µ fading channel is used to derive analytical expressions for the ergodic capacity and outage probability. Our proposed analysis considers not only the IRS reflected channels, but also the direct channel between the communication nodes. The results of the derived analytical expressions are validated through Monte Carlo simulations. Through simulations, it has been noticed that pointing errors degrade the performance of the IRS-assisted THz wireless communication system which can be compensated by deploying an IRS having a large number of reflecting elements.

Virus-Induced Gene Silencing (VIGS): A Powerful Tool for Crop Improvement and Its Advancement towards Epigenetics.

Virus-induced gene silencing (VIGS) is an RNA-mediated reverse genetics technology that has evolved into an indispensable approach for analyzing the function of genes. It downregulates endogenous genes by utilizing the posttranscriptional gene silencing (PTGS) machinery of plants to prevent systemic viral infections. Based on recent advances, VIGS can now be used as a high-throughput tool that induces heritable epigenetic modifications in plants through the viral genome by transiently knocking down targeted gene expression. As a result of the progression of DNA methylation induced by VIGS, new stable genotypes with desired traits are being developed in plants. In plants, RNA-directed DNA methylation (RdDM) is a mechanism where epigenetic modifiers are guided to target loci by small RNAs, which play a major role in the silencing of the target gene. In this review, we described the molecular mechanisms of DNA and RNA-based viral vectors and the knowledge obtained through altering the genes in the studied plants that are not usually accessible to transgenic techniques. We showed how VIGS-induced gene silencing can be used to characterize transgenerational gene function(s) and altered epigenetic marks, which can improve future plant breeding programs.

Computational Studies on Diverse Characterizations of Molecular Descriptors for Graphyne Nanoribbon Structures.

Materials made of graphyne, graphyne oxide, and graphyne quantum dots have drawn a lot of interest due to their potential uses in medicinal nanotechnology. Their remarkable physical, chemical, and mechanical qualities, which make them very desirable for a variety of prospective purposes in this area, are mostly to blame for this. In the subject of mathematical chemistry, molecular topology deals with the algebraic characterization of molecules. Molecular descriptors can examine a compound's properties and describe its molecular topology. By evaluating these indices, researchers can predict a molecule's behavior including its reactivity, solubility, and toxicity. Amidst the captivating realm of carbon allotropes, γ-graphyne has emerged as a mesmerizing tool, with exquisite attention due to its extraordinary electronic, optical, and mechanical attributes. Research into its possible applications across numerous scientific and technological fields has increased due to this motivated attention. The exploration of molecular descriptors for characterizing γ-graphyne is very attractive. As a result, it is crucial to investigate and predict γ-graphyne's molecular topology in order to comprehend its physicochemical characteristics fully. In this regard, various characterizations of γ-graphyne and zigzag γ-graphyne nanoribbons, by computing and comparing distance-degree-based topological indices, leap Zagreb indices, hyper leap Zagreb indices, leap gourava indices, and hyper leap gourava indices, are investigated.

Renewable Electricity Enables Green Routes to Fine Chemicals and Pharmaceuticals.

Syntheses of chemicals using renewable electricity and when generating high atom economies are considered green and sustainable processes. In the present state of affairs, electrochemical manufacturing of fine chemicals and pharmaceuticals is not as common place as it could be and therefore, merits more attention. There is also a need to turn attention toward the electrochemical synthesis of valuable chemicals from recyclable greenhouse gases that can accelerate the process of circular economy. CO2 emissions are the major contributor to human-induced global warming. CO2 conversion into chemicals is a valuable application of its utilisation and will contribute to circular economy while maintaining environmental sustainability. Herein, we present an overview of electro-carboxylation, including mechanistic aspects, which forms carboxylic acids using molecular carbon dioxide. We also discuss atom economies of electrochemical fluorination, methoxylation and amide formation reactions.

Enhanced field-dependent conductivity and material properties of nano-AlN/micro-SiC/silicone elastomer hybrid composites for electric stress mitigation in high-voltage power modules.

This paper reports an enhancement of the nonlinear conductivity, thermal and mechanical properties of micro-silicon carbide/silicone elastomer (m-SiC/SE) composites by adding nano-aluminum nitride (n-AlN) for power module encapsulation applications. The electrical properties (such as nonlinear conductivity characteristics and transient permittivity obtained from polarization current, and trap distributions obtained from thermally stimulated depolarization current) and material properties (including thermo-gravimetric analysis, coefficient of thermal expansion (CTE), and thermal conductivity, tensile strength, strain at break and Young's modulus) of the pure SE, m-SiC/SE microcomposites, m-SiC/n-AlN/SE hybrid composites are investigated. The effect of the m-SiC fillers and n-AlN fillers on physicochemical properties of the SE matrix is analyzed by FT-IR spectroscopy and crosslinking degree. The measured nonlinear conductivity and transient permittivity are used for electric field simulation under DC stationary and square voltages. It is found that the addition of n-AlN fillers in the SE hybrid composite improves the nonlinear conductivity characteristics and mitigates the electric field under DC stationary and square voltages, compared to the SE microcomposite. Furthermore, the m-SiC/n-AlN/SE hybrid composite has a higher thermal degradation temperature, thermal conductivity, tensile strength, Young's modulus, and crosslinking degree than the SE microcomposite, whereas their CTE and strain at break are lower. It is elucidated that the m-SiC/n-AlN/SE hybrid composite with enhanced nonlinear conductivity and material properties is a promising packaging material for high-voltage power modules.

Current challenges of hydrothermal treated wastewater (HTWW) for environmental applications and their perspectives: A review.

Hydrothermal treatment (HT) is an emerged thermochemical approach for the utilization of biomass. In the last decade, intense research has been conducted on bio-oil and hydrochar, during which extensive amount of hydrothermal treated wastewater (HTWW) is produced, containing large amount of organic compounds along with several toxic chemicals. The composition of HTWW is highly dependent on the process conditions and organic composition of biomass, which determines its further utilization. The current study provides a comprehensive overview of recent advancements in HTWW utilization and its properties which can be changed by varying different parameters like temperature, residence time, solid concentration, mass ratio and catalyst including types of biomasses. HTWW characterization, parameters, reaction mechanism and its application were also summarized. By considering the challenges of HTWW, some suggestions and proposed methodology to overcome the bottleneck are provided.

Assessing the impact of diversity and ageing population on health expenditure of United States.

BACKGROUND: At the biological level, ageing results from a plodding decline in physical and mental capability, an emergent menace of malady, and eventually, fatality. Even though a few of the geriatric's health changes are hereditary, to a great extent is due to individual's physical and societal surroundings and their residence, locality, societies, gender, ethnicity or socio-economic status. The current debate is well popular by the relationship between increasing diversity and the ageing population with healthcare expenditure in the United States. Higher diversity in society and increasing ageing population have various socio-economic consequences. A good policy in this regard helpful to managed and get fruitful outcomes. OBJECTIVE: This study aims to examine the direct effects of diversity and ageing population on healthcare spending. The assortment observed in geriatrics is not arbitrary. A huge portion emerges from individual's physical and social settings and the influence of these environs on their prospect and well-being demeanour. METHOD: This study used the Bayesian-vector autoregressive model, impulse response analysis, and variance decomposition and data over the period 1990-2018 for empirical analysis of the United States. RESULTS: The empirical findings indicate that diversity and ageing population are more persistent with health expenditure in the United States. This study concludes that an increase in diversity and ageing population will rely on the long-term healthcare facility. CONCLUSION: The study suggests that cohesive society and effective health intervention might aid in curtailing expenditure pressure linked with elderly population. Furthermore, a recommendation of this study is a good opportunity for healthcare policymakers and further researches.

Imputing Missing Data in Hourly Traffic Counts.

Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.

Investigating the Impact of Information Sharing in Human Activity Recognition.

The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.

A Fast Electrical Resistivity-Based Algorithm to Measure and Visualize Two-Phase Swirling Flows.

Electrical resistance tomography (ERT) has been used in the literature to monitor the gas-liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the relevant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10× less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000× faster than solving the inverse problem non-iteratively via the Gauss-Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the average estimation error below 6 mm in the worst-case scenario.

Efficient Matching-Based Parallel Task Offloading in IoT Networks.

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods.

The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550-3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels' attributes and the diversification of GAA users' business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale-Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

Strain-Dependent Photoacoustic Characteristics of Free-Standing Carbon-Nanocomposite Transmitters.

In this paper we demonstrate strain-dependent photoacoustic (PA) characteristics of free-standing nanocomposite transmitters that are made of carbon nanotubes (CNT) and candle soot nanoparticles (CSNP) with an elastomeric polymer matrix. We analyzed and compared PA output performances of these transmitters which are prepared first on glass substrates and then in a delaminated free-standing form for strain-dependent characterization. This confirms that the nanocomposite transmitters with lower concentration of nanoparticles exhibit more flexible and stretchable property in terms of Young's modulus in a range of 4.08-10.57 kPa. Then, a dynamic endurance test was performed revealing that both types of transmitters are reliable with pressure amplitude variation as low as 8-15% over 100-800 stretching cycles for a strain level of 5-28% with dynamic endurance in range of 0.28-2.8%. Then, after 2000 cycles, the transmitters showed pressure amplitude variation of 6-29% (dynamic endurance range of 0.21-1.03%) at a fixed strain level of 28%. This suggests that the free-standing nanocomposite transmitters can be used as a strain sensor under a variety of environments providing robustness under repeated stretching cycles.