The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery.

Trifluoroacetic acid-mediated synthesis of xanthene constructs and their extensive anti-tuberculosis evaluation.

A diverse range of 9-substituted 1,8-dioxohexahydroxanthenes was conceptualized and synthesized through a TFA-mediated approach in near quantitative yields without the use of column chromatography. From a series of 25 compounds, we found that compounds 14c and 14r exhibited promising anti-tuberculosis potential against avirulent and virulent strains of Mycobacterium tuberculosis with a Minimal Inhibitory Concentration (MIC) of 8 µg ml-1, achieving 99% bactericidal activity at the same concentration. This series of compounds was found to be inactive against common Gram-positive and Gram-negative pathogens, indicating that the activity is mycobacteria-specific. Since the strategies for treating tuberculosis employ a combinatorial therapy, we tested and observed that the two lead compounds displayed synergistic behavior with known anti-TB drugs (ATDs) and a significant (16-32 fold) decrease in MIC values of both leads was observed in combination with either RIF or INH. Interestingly the lead molecule 14c displayed only time-dependent kill kinetics and sterilized the whole culture of Mycobacterium tuberculosis H37Rv in just 48 hours.

Actinomycetes - The Repertoire of Diverse Bioactive Chemical Molecules: From Structures to Antibiotics.

The urgent need for novel antibiotics in the face of escalating global antimicrobial resistance necessitates innovative approaches to identify bioactive compounds. Actinomycetes, renowned for their prolific production of antimicrobial agents, stand as a cornerstone in this pursuit. Their diverse metabolites exhibit multifaceted bioactivities, including potent antituberculosis, anticancer, immunomodulatory, immuno-protective, antidiabetic, etc. Though terrestrial sources have been exploited significantly, contemporary developments in the field of antimicrobial drug discovery have put marine actinomycetes in a prominent light as a promising and relatively unexplored source of novel bioactive molecules. This is further boosted by post-genomic era advances like bioinformatics-based secretome analysis and reverse engineering that have totally revitalized actinomycetes antibiotic research. This review highlights actinomycetes-based chemically diverse scaffolds and clinically validated antibiotics along with the enduring significance of actinomycetes from untouched ecosystems, especially with recent advanced techniques in the quest for next-generation antimicrobials.

Facile synthesis of manganese-hafnium nanocomposites for multimodal MRI/CT imaging and in vitro photodynamic therapy of colon cancer.

Precise diagnosis of complex and soft tumors is challenging, which limits appropriate treatment options to achieve desired therapeutic outcomes. However, multifunctional nano-sized contrast enhancement agents based on nanoparticles improve the diagnosis accuracy of various diseases such as cancer. Herein, a facile manganese-hafnium nanocomposites (Mn3O4-HfO2 NCs) system was designed for bimodal magnetic resonance imaging (MRI)/computed tomography (CT) contrast enhancement with a complimentary function of photodynamic therapy. The solvothermal method was used to fabricate NCs, and the average size of Mn3O4 NPs and Mn3O4-HfO2 NCs was about 7 nm and 15 nm, respectively, as estimated by TEM. Dynamic light scattering results showed good dispersion and high negative (-33 eV) zeta potential, indicating excellent stability in an aqueous medium. Mn3O4-HfO2 NCs revealed negligible toxic effects on the NCTC clone 929 (L929) and mouse colon cancer cell line (CT26), demonstrating promising biocompatibility. The synthesized Mn3O4-HfO2 NCs exhibit significant enhancement in T1-weighted magnetic resonance imaging (MRI) and X-ray computed tomography (CT), indicating the appropriateness for dual-modal MRI/CT molecular imaging probes. Moreover, ultra-small Mn3O4-HfO2 NCs show good relaxivities for MRI/CT. These nanoprobes Mn3O4-HfO2 NCs further possessed outstanding reactive oxygen species (ROS) generation ability under minute ultraviolet light (6 mW·cm-2) to ablate the colon cancer cells in vitro. Therefore, the designed multifunctional Mn3O4-HfO2 NCs were ideal candidates for cancer diagnosis and photodynamic therapy.

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis.

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.

Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network.

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump's inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time-frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network.

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

Deciphering Tuberculous Meningitis: From Clinical Challenges to Novel Models and Pathogenic Pathways.

During and after the COVID-19 pandemic,Tuberculosis (TB) has reestablished with higher figures due to interruptions in the Directly Observed Treatment Short course (DOTS) despite underreporting. The rising consequences would have extended to extra-pulmonary forms of TB as well, including Tuberculous Meningitis (TBM). Considering the fact that TBM is the most dangerous and worst form of TB, we found the need to scan the literature to highlight various aspects of TBM. Epidemiology of TBM is proportionally less frightening, but the consequent mortalities and morbidities are more alarming than pulmonary TB. Here, we address critical research gaps in Tuberculous Meningitis that warrant further investigations. The highlighted aspects encompass a comprehensive understanding of TBM's clinical presentation and improved diagnostic tools for timely detection, the exploration of innovative chemotherapies and surgical interventions, the unraveling of the role of the blood-brain barrier in disease onset, investigating of the contributions of various brain cells to TBM development, deciphering the complex inflammatory response, exploring the involvement of Matrix Metalloproteinases in tissue damage, delving into host-pathogen genetics influencing susceptibility, utilizing robust in-vivo and in-vitro models for mechanistic insights, and more importantly between TBM and SARS-COVID-19 are discussed. Addressing these gaps will substantially advance our understanding of TBM's complex pathogenesis, contributing to more effective diagnostic, therapeutic, and preventive strategies against this debilitating disease.

A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov-Smirnov Test.

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov-Smirnov (K-S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K-S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

Physicochemical Properties and Simulation of Magnesium/Zinc Binary-Substituted Hydroxyapatite with Enhanced Biocompatibility and Antibacterial Ability.

Ionic substitution can effectively activate the surface of hydroxyapatite (HA) for bone repair and regeneration processes. Therefore in this study, magnesium (Mg)-, zinc (Zn)-, and Mg/Zn-codoped HA was prepared by a hydrothermal method. The results of experimental and first-principles calculations verify the existence of Mg and Zn ions in the HA structure by altering cell parameters, crystallinity, and particle size. The results also showed that Mg and Zn are actively accommodated at the Ca(1) and Ca(2) positions, which not only inhibit HA formation but also promote calcium-deficient HA, and when the codoping content increased to 10%Mg and 10%Zn, the HA transformed completely to the whitlockite phase. Furthermore, the impact of codoping on biocompatibility was examined by employing MC3T3 cells. The in vitro study revealed that 5%Mg and 5%Zn single and -codoped HA promoted the proliferation of MC3T3 cells and 5%Mg-doped and -codoped HA stimulated MC3T3 cell differentiation, while 5%Zn-doped and -codoped HA revealed worthy antibacterial properties. Overall, the obtained results demonstrate that cosubstituted HA (5%Mg and 5%Zn) is promising, which not only eradicates bacteria (Escherichia coli and Staphylococcus aureus) but also induces bone regeneration. These findings suggest that 5%Mg and 5%Zn binary-substituted HA is a very promising biomaterial for hard tissue scaffolds and bone repair.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest.

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy.

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann-Whitney Test.

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann-Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann-Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann-Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann-Whitney test form the new fault-specific Mann-Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Molecular Insights into the In Vivo Analgesic and Anti-Inflammatory Activity of Indomethacin Analogues.

The primary objective of this research was to identify and explore the most potent and efficacious cyclooxygenase inhibitors, utilizing indole acetic acid drugs as a lead molecule. To achieve this objective, various derivatives (2a-2c and 2e-2g) of the selected lead molecule, indomethacin, were synthesized using a reflux condensation process, targeting the hydroxyl group. The synthesized analogues were subjected to different spectroscopic procedures to determine their structure and confirm their analogues. These derivatives were further screened for acute toxicity and anti-nociceptive and anti-inflammatory activity using established protocols. Docking analysis was performed to evaluate the possible protein-ligand interaction. The test compounds were found to be safe at doses of 50, 75, 100, and 200 mg/kg, i.p. The pharmacological screening revealed that test compounds 2a-2f had a superior peripheral analgesic effect at a dose of 10 mg/kg, in comparison to the parent drug indomethacin, while compound 2g exhibited slightly lower activity at the same dose. The hot plate results showed lower central analgesic activity of the test compounds compared to the standard Tramal, but it was still significant. Anti-inflammatory results were significant, comparable to Diclofenac sodium and indomethacin, except for compounds 2b, 2c, and 2e at a dose of 10 mg/kg body weight. Molecular docking analysis demonstrated that the derived compounds had augmented negative binding energies (-149.39, -146.72, -160.85, -159.34, -140.03, and -150.91 KJ/mol) compared to the parent drugs (-141.07), which supported the research's theme of producing stronger derivatives of standard drugs with significant anti-nociceptive and anti-inflammatory potential. The derived compounds exhibited significant analgesic and anti-inflammatory activities and, therefore, have the potential to be studied further as new drug candidates for pain and inflammation.

Exogenously applied nicotinic acid alleviates drought stress by enhancing morpho-physiological traits and antioxidant defense mechanisms in wheat.

Across the globe, the frequent occurrence of drought spells has significantly undermined the sustainability of modern high-input farming systems, particularly those focused on staple crops like wheat. To ameliorate the deleterious impacts of drought through a biologically viable and eco-friendly approach, a study was designed to explore the effect of nicotinic acid on different metabolic, and biochemical processes, growth and yield of wheat under optimal moisture and drought stress (DS). The current study was comprised of different levels of nicotinic acid applied as foliar spray (0 g L-1, 0.7368, 1.477, 2.2159 g L-1) and fertigation (0.4924, 0.9848, and 1.4773 g L-1) under normal conditions and imposed drought by withholding water at anthesis stage. The response variables were morphological traits such as roots and shoots characteristics, yield attributes, grain and biological yields along with biosynthesis of antioxidants. The results revealed that nicotinic acid dose of 2.2159 g L-1 out-performed rest of treatments under both normal and DS. The same treatment resulted in the maximum root growth (length, fresh and dry weights, surface area, diameter) and shoot traits (length, fresh and dry weights) growth. Additionally, foliar applied nicotinic acid (2.2159 g L-1) also produced as the highest spike length, grains spike-1, spikelet's spike-1 and weight of 1000 grains. Moreover, these better yield attributes led to significantly higher grain yield and biological productivity of wheat. Likewise in terms of physiological growth of wheat under DS, the same treatment remained superior by recording the highest SPAD value, relative water content, water potential of leaves, leaf area, stomatal conductance (292 mmolm-2S-1), internal carbon dioxide concentration, photosynthesis and transpiration rate. Interestingly, exogenously applied nicotinic acid remained effective in triggering the antioxidant system of wheat by recording significantly higher catalase, peroxidase, superoxide dismutase and ascorbate peroxidase.

Synthesis, Characterization, and Antimicrobial Activity of Ultra-Short Cationic ß-Peptides.

The development of new antibiotics is urgently required because of the rapidly growing resistance against conventional antibiotics. The antimicrobial peptides show potential as small antibiotic molecules. The stability of peptides is a primary concern for the use of peptides as drugs. Introducing ß-amino acids into peptide sequences can be useful in preventing biological degradation by proteolytic enzymes. Herein, we describe the synthesis, characterization, and antimicrobial activity of ultra-short cationic ß-peptides, LA-ß3,3-Pip-ß2,2-Ac6c-PEA, P1; LA-ß3,3-Pip(G)-ß2,2-Ac6c-PEA, P2; LAU-ß3,3-Pip-ß2,2-Ac6c-PEA, P3, and LAU-ß3,3-Pip(G)-ß2,2-Ac6c-PEA, P4. Peptides P1-P4 were evaluated against Gram-negative, Gram-positive, MRSA, and multi-drug resistant E. coli (MDR-E. coli). P3 exhibited the most potent antimicrobial activity against E. coli, S. epidermidis, S. aureus, K. pneumoniae, S. mutans, and E. faecalis, with MIC values 0.5, 2, 0.5, 1, 2, and 1 µg/mL, respectively. P3 exhibited time- and concentration-dependent bactericidal activities against E. coli, S. aureus, and E. faecalis with a killing rate of 1.6 logs/h. The treatment of E. coli with peptide P3 showed membrane disruption. In addition, P3 exhibited the inhibition of biofilm produced by E. coli, synergism with antibiotics (ciprofloxacin, streptomycin, and ampicillin), 100% cell viability against AML12, RAW 264.7, and HEK-293 cell lines at 1, and 10 µg/mL concentrations.

Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN.

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods.

Dose optimization of silicon for boosting arbuscular mycorrhizal fungi colonization and cadmium stress mitigation in maize (Zea mays L.).

The foliar applied silicon (Si) has the potential to ameliorate heavy metals, especially cadmium (Cd) toxicity; however, Si dose optimization is strategically important for boosting the growth of soil microbes and Cd stress mitigation. Thus, the current research was performed to assess the Si-induced physiochemical and antioxidant trait alterations along with Vesicular Arbuscular Mycorrhiza (VAM) status in maize roots under Cd stress. The trial included foliar Si application at the rate of 0, 5, 10, 15, and 20 ppm while Cd stress (at the rate of 20 ppm) was induced after full germination of maize seed. The response variables included various physiochemical traits such as leaf pigments, protein, and sugar contents along with VAM alterations under induced Cd stress. The results revealed that exogenous application of Si in higher doses remained effective in improving the leaf pigments, proline, soluble sugar, total proteins, and all free amino acids. Additionally, the same treatment remained unmatched in terms of antioxidant activity compared to lower doses of foliar-applied Si. Moreover, VAM was recorded to be at peak under 20 ppm Si treatment. Thus, these encouraging findings may serve as a baseline to develop Si foliar application as a biologically viable mitigation strategy for maize grown in Cd toxicity soils. Overall, the exogenous application of Si helpful for reducing the uptake of Cd in maize and also improving the mycorrhizal association as well as the philological mechanism and antioxidant activities in plant under cadmium stress conditions. Also, future studies must test more doses concerning to varying Cd stress levels along with determining the most responsive crop stage for Si foliar application.

Exogenous application of low and high molecular weight organic acids differentially affected the uptake of cadmium in wheat-rice cropping system in alkaline calcareous soil.

Anthropogenic cadmium (Cd) in arable soils is becoming a global concern due to its harmful effects on crop yield and quality. The current study examined the role of exogenously applied low molecular weight organic acids (LMWOAs) including oxalic acid (OxA), tartaric acid (TA) and high molecular weight organic acids (HMWOAs) like citric acid (CA) and humic acid (HA) for the bioavailability of Cd in wheat-rice cropping system. Maximum increase in root dry-weight, shoot dry-weight, and grain/paddy yields was recorded with HA for both crops. The HA significantly decreased AB-DTPA Cd in contaminated soils which remained 41% for wheat and 48% for rice compared with their respective controls. The minimum concentration of Cd in roots, shoots and grain/paddy was observed in HA treatment in both crops. The organic acids significantly increased the growth parameters, photosynthetic activity, and relative leaf moisture contents for both wheat and rice crops compared to that with the contaminated control. Application of OxA and TA increased the bioavailability of Cd in soils and plant tissues while CA and HA decreased the bioavailability of Cd in soils and plants. The highest decrease in Cd uptake, bioaccumulation, translocation factor, immobilization, translocation, harvest, and health risk indices were observed with HA while maximum increase was recorded with OxA for both wheat and rice. The results concluded that use of HMWOAs is effective in soil Cd immobilization being maximum with HA. While LMWOAs can be used for the phytoextraction of Cd in contaminated soils having maximum potential with OxA.