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.

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.

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.

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.

Genetic variations among the isolates of Bipolaris Maydis based on phenotypic and molecular markers

Abstract Maydis leaf blight, caused by Bipolaris maydis, is an important disease of maize crop in Khyber Pakhtunkhwa (KP) Pakistan. Fifteen isolates of the pathogen, collected across KP, were studied for variability based on phenotypic and molecular markers. Significant variability among the isolates was observed when assessed using phenotypic traits such as radial growth, spore concentration, fungicide sensitivity and virulence. The isolates were classified into six culture groups based on colour, texture and margins of the colony. Conidial morphology was also variable. These were either straight or slightly curved and light to dark brown in colour. Fungicide test showed significant variation in the degree of sensitivity against Carbendazim. Isolate Bm8 exhibited maximum radial growth on carbendazim spiked plates. Conversely, isolate Bm15 showed the lowest radial growth. Variations in virulence pattern of the isolates were evident when a susceptible maize variety Azam was inoculated with spores of B. maydis. Genetic variability amongst the isolates was also estimated by RAPD as well as sequencing of ITS region. The RAPD dendrogram grouped all the isolates into two major clusters. Average genetic distance ranged from 0.6% to 100%, indicating a diverse genetic gap among the isolates. Maximum genetic distance was found between isolates Bm9 and Bm10 as well as Bm2 and Bm8. Conversely, isolates Bm13 and Bm15 were at minimum genetic distance. Phylogenetic dendrogram based on sequencing of ITS region grouped all the isolates into a single major cluster. The clusters in both the dendrogram neither correlate to the geographical distribution nor to the morphological characteristics.

Genomic characterization of Puccinia triticina using molecular marker technology

Abstract Leaf rust, caused by Puccinia triticina, is the most common rust disease of wheat. The fungus is an obligate parasite capable of producing infectious urediniospores. To study the genetic structure of the leaf rust population 20 RAPD primers were evaluated on 15 isolates samples collected in Pakistan. A total of 105 RAPD fragments were amplified with an average of 7 fragments per primer. The number of amplified fragments varied from 1 to 12. GL Decamer L-07 and GL Decamer L-01 amplified the highest number of bands (twelve) and primer GL Decamer A-03 amplified the lowest number of bands i.e one. Results showed that almost all investigated isolates were genetically different that confirms high genetic diversity within the leaf rust population. Rust spores can follow the migration pattern in short and long distances to neighbor areas. Results indicated that the greatest variability was revealed by 74.9% of genetic differentiation within leaf rust populations. These results suggested that each population was not completely identical and high gene flow has occurred among the leaf rust population of different areas. The highest differentiation and genetic distance among the Pakistani leaf rust populations were detected between the leaf rust population in NARC isolate (NARC-4) and AARI-11and the highest similarity was observed between NARC isolates (NARC-4) and (NARC-5). The present study showed the leaf rust population in Pakistan is highly dynamic and variable.

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.

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.

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.

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.

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.

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.

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.

Adenoid hypertrophy-​diagnosis and treatment: the new S2k guideline

Hyperplasia of the pharyngeal tonsils is to be considered pathologic when nasopharyngeal symptoms of mechanical obstruction and/or chronic inflammation occur. Chronic Eustachian tube dysfunction can result in various middle ear diseases such as conductive hearing loss, cholesteatoma, and recurrent acute otitis media. During examination, attention should be paid to the presence of adenoid facies (long face syndrome), with a permanently open mouth and visible tip of the tongue. In the case of severe symptoms and/or failure of conservative treatment, adenoidectomy is usually performed on an outpatient basis. Conventional curettage remains the established standard treatment in Germany. Histologic evaluation is indicated for clinical evidence of mucopolysaccharidoses. Due to the risk of hemorrhage, the preoperative bleeding questionnaire, which is obligatory before every pediatric surgery, is referred to. Recurrence of adenoids is possible despite correct adenoidectomy. Before discharge home, otorhinolaryngologic inspection of the nasopharynx for secondary bleeding should be performed and anesthesiologic clearance obtained.

Synthesis, Characterization, and Antimicrobial Activity of Ultra-Short Cationic β-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 β-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 β-peptides, LA-β3,3-Pip-β2,2-Ac6c-PEA, P1; LA-β3,3-Pip(G)-β2,2-Ac6c-PEA, P2; LAU-β3,3-Pip-β2,2-Ac6c-PEA, P3, and LAU-β3,3-Pip(G)-β2,2-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.

Differential response of nano zinc sulphate with other conventional sources of Zn in mitigating salinity stress in rice grown on saline-sodic soil

Salinization causes the degradation of the soil and threatening the global food security but the application of essential micronutrients like zinc (Zn), improve the plant growth by stabilizing the plant cell and root development. Keeping in view the above-mentioned scenario, an experiment was conducted to compare the efficiency of conventional Zn fertilizers like zinc sulphate (ZnSO₄), zinc ethylene diamine tetra acetic acid (Zn-EDTA) and advance nano Zn fertilizers such as zinc sulphate nanoparticles (ZnSO₄NPs), and zinc oxide nanoparticles (ZnONPs) (applied at the rate of 5 and 10 mg/kg) in saline-sodic soil. Results revealed that the maximum plant height (67%), spike length (72%), root length (162%), number of tillers (71%), paddy weight (100%), shoot dry weight (158%), and root dry weight (119%) was found in ZnSO₄NPs applied at the rate of 10 mg/kg (ZnSO₄NPs-10) as compared to salt-affected control (SAC). Similarly, the plants physiological attributes like chlorophyll contents (91%), photosynthesis rate (113%), transpiration rate (106%), stomatal conductance (56%) and internal CO₂ (11%) were increased by the application of ZnSO₄NPs-10, as compared to SAC. The maximum Zn concentration in root (153%), shoot (205%) and paddy (167%) found in ZnSO₄NPs-10, as compared to control. In the body of rice plants, other nutrients like phosphorus and potassium were also increased by the application of ZnSO₄NPs-10 and soil chemical attributes such as sodium and sodium adsorption ratio were decreased. The current experiment concluded that the application of ZnSO₄NPs at the rate of 10 mg/kg in salt-affected paddy soil increased the growth, physiology, up take of essential nutrients and yield of rice by balancing the cationic ratio under salt stress.

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.

[Adenoids-diagnosis and treatment: the new German S2k guideline]

Hyperplasia of the pharyngeal tonsils is to be considered pathologic when nasopharyngeal symptoms of mechanical obstruction and/or chronic inflammation occur. Chronic Eustachian tube dysfunction can result in various middle ear diseases such as conductive hearing loss, cholesteatoma, and recurrent acute otitis media. During examination, attention should be paid to the presence of adenoid facies (long face syndrome), with a permanently open mouth and visible tip of the tongue. In the case of severe symptoms and/or failure of conservative treatment, adenoidectomy is usually performed on an outpatient basis. Conventional curettage remains the established standard treatment in Germany. Histologic evaluation is indicated for clinical evidence of mucopolysaccharidoses. Due to the risk of hemorrhage, the preoperative bleeding questionnaire, which is obligatory before every pediatric surgery, is referred to. Recurrence of adenoids is possible despite correct adenoidectomy. Before discharge home, otorhinolaryngologic inspection of the nasopharynx for secondary bleeding should be performed and anesthesiologic clearance obtained.

Foliar application of silicon-based nanoparticles improve the adaptability of maize (Zea mays L.) in cadmium contaminated soils

Heavy metals (HMs) especially cadmium (Cd) absorbed by the roots of crop plants like maize have emerged as one of the most serious threats by causing stunted plant growth along with disturbing the photosynthetic machinery and nutrient homeostasis process. A trial was conducted for inducing Cd stress tolerance in maize by exogenous application of silicon nanoparticles (SiNPs) using five doses of SiNPs (0, 100, 200, 300, and 400 ppm) and three levels of Cd (0, 15, and 30 ppm) for maize hybrid (SF-9515). The response variables included morphological traits and biochemical parameters of maize. The results indicated that Cd level of 30 ppm remained the most drastic for maize plants by recording the minimum traits such as shoot length (39.35 cm), shoot fresh weight (9.52 g) and shoot dry weight (3.20 g), leaf pigments such as chlorophyll a (0.55 mg/g FW), chlorophyll b (0.27 mg/g FW), total contents (0.84 mg/g FW), and carotenoid contents (0.19 µg/g FW). Additionally, the same Cd level disrupted biochemical traits such as TSP (4.85 mg/g FW), TP (252.94 nmol/g FW), TSAA (18.92 µmol g^-1 FW), TSS (0.85 mg/g FW), and antioxidant activities such as POD (99.39 min^-1 g^-1 FW), CAT (81.58 min^-1 g^-1 FW), APX (2.04 min^-1 g^-1 FW), and SOD (172.79 min^-1 g^-1 FW). However, a higher level of Cd resulted in greater root length (87.63 cm), root fresh weight (16.43 g), and root dry weight (6.14 g) along with higher Cd concentration in the root (2.52 µg/g^-1) and shoot (0.48 µg/g^-1). The silicon nanoparticles (Si NPs) treatment significantly increased all measured attributes of maize. The highest value was noted of all the parameters such as chlorophyll a (0.91 mg/g FW), chlorophyll b (0.57 mg/g FW), total chlorophyll contents (1.48 mg/g FW), total carotenoid contents (0.40 µg/g FW), TSP (6.12 mg/g FW), TP (384.56 nmol/g FW), TSAA (24.64 µmol g^-1 FW), TSS (1.87 mg/g FW), POD (166.10 min^-1 g^-1 FW), CAT (149.54 min^-1 g^-1 FW), APX (3.49 min^-1 g^-1 FW), and SOD (225.57 min^-1 g^-1 FW). Based on recorded findings, it might be inferred that higher levels of Cd tend to drastically reduce morpho-physiological traits of maize and foliage-applied silver nanoparticles hold the potential to ameliorate the adverse effect of Cd stress on maize.

Amylase as a Diagnostic Tool for Plunging Ranula: Clinical Series and Description of the Technique

OBJECTIVES

This study describes a technique of measurement for neck cyst amylase content and reviews the experience of a tertiary referral center for cases of suspected plunging ranula.

METHODS

A retrospective study was performed at the Manukau Surgical Center in Auckland, New Zealand. Patients with a possible diagnosis of plunging ranula based on clinical presentation and diagnostic aspiration of the cyst contents were included. Demographic data, imaging and laboratory findings were collected, along with findings from surgery and histology. The technique for measuring the amylase of the aspirated cyst contents was also carefully recorded.

RESULTS

The 37 cases of confirmed plunging ranula included in this study had a submandibular cystic swelling that was aspirated. Imaging features consistent with a plunging ranula were seen in 89% of the study group. All cases had detectable levels of amylase of ≥3 U/L in the ranula contents. There was large variability (range: 5-560 U/L) in the concentration of amylase, with 70% of the cases demonstrating an amylase concentration below 200 U/L. Aspirates were typically described as viscous (87.5%) and yellow or straw-colored.

CONCLUSION

The combination of clinical presentation, imaging and the presence of amylase in the cyst contents is diagnostic for plunging ranula.

LEVEL OF EVIDENCE

4 Laryngoscope, 133:535-538, 2023.

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

Leak Localization on Cylinder Tank Bottom Using Acoustic Emission

In this study, a scheme for leak localization on a cylinder tank bottom using acoustic emission (AE) is proposed. This approach provides a means of early failure detection, thus reducing financial damage and hazards to the environment and users. The scheme starts with the hit detection process using a constant false alarm rate (CFAR) and a fixed thresholding method for a time of arrival (TOA) and an end-time determination. The detected hits are then investigated to group those originating from the same AE source together by enforcing an event definition and a similarity score. Afterwards, these newly grouped hits are processed by a time difference of arrival (TDOA) to find the locations of the events. Since the locations of the events alone do not pinpoint the leak location, a data density analysis using a Voronoi diagram is employed to find the area with the highest possibility of a leak's existence. The proposed method was validated using the Hsu-Nielsen test on a cylinder tank bottom under a one-failed-sensor scenario, which returned a highly accurate result across multiple test locations.

Enhancing drought stress tolerance in Camelina (Camelina sativa L.) through exogenous application of potassium

The current study was performed under controlled conditions to study the effects of exogenous potassium application on carotenoid contents and drought tolerance in Camelina. Water deficit levels such as 100% FC (control) and 40% FC (drought stress) were imposed after germination of Camelina plants grown to maturity, and different treatments of exogenous K+ were applied at the vegetative stage. We have reported 17 traits of plant growth, physiology, antioxidant enzyme activity, focusing on carotenoids in Camelina to explore their potential yield and yield components. For this purpose, we used multivariate analysis techniques (descriptive statistics, correlation matrix, analysis of variance [ANOVA] and principal components analysis [PCA] to determine the best relation between potassium and studied traits). The results showed a large number of variations in the studied trait under control and water deficit condition. Plant fresh weight (g) was negatively correlated with shoot length and SOD insignificantly correlated with plant fresh weight (g) under water deficit conditions. Potassium loading predicted that foliar application (3 mM K₂ SO₄ ), foliar application (6 mM KNO₃ ), foliar application (12 mM KNO₃ ) and foliar application (12 mM K₂ SO₄ ) are the important doses that contribute the most to enhance the growth, physiological and biochemical activities and carotenoids to improve the Camelina yield under water deficit condition. These doses should be considered in the future to improve the Camelina yield under semi-arid conditions with increased genetic diversity (varietal selection).

A Centrifugal Pump Fault Diagnosis Framework Based on Supervised Contrastive Learning

A novel intelligent centrifugal pump (CP) fault diagnosis method is proposed in this paper. The method is based on the contrast in vibration data obtained from a centrifugal pump (CP) under several operating conditions. The vibration signals data obtained from a CP are non-stationary because of the impulses caused by different faults; thus, traditional time domain and frequency domain analyses such as fast Fourier transform and Walsh transform are not the best option to pre-process the non-stationary signals. First, to visualize the fault-related impulses in vibration data, we computed the kurtogram images of time series vibration sequences. To extract the discriminant features related to faults from the kurtogram images, we used a deep learning tool convolutional encoder (CE) with a supervised contrastive loss. The supervised contrastive loss pulls together samples belonging to the same class, while pushing apart samples belonging to a different class. The convolutional encoder was pretrained on the kurtograms with the supervised contrastive loss to infer the contrasting features belonging to different CP data classes. After pretraining with the supervised contrastive loss, the learned representations of the convolutional encoder were kept as obtained, and a linear classifier was trained above the frozen convolutional encoder, which completed the fault identification. The proposed model was validated with data collected from a real industrial testbed, yielding a high classification accuracy of 99.1% and an error of less than 1%. Furthermore, to prove the proposed model robust, it was validated on CP data with 3.0 and 3.5 bar inlet pressure.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

Stibonic acids and related stibonate-phosphonate clusters: Synthesis, characterization and bioactivity evaluation

The reaction of 4-(azobenzene)phenylstibonic acid with t-butylphosphonic acid led to the isolation of the tetranuclear oxo-hydroxo antimony cluster of formulae [(4-azobenzene-C₆H₄Sb)₄(OH)₄(^tBuPO₃)₆] (C1). The reaction of (p-t-butyl phenyl stibonic acid with phenyl phosphonic acid resulted in the isolation of complex with formulae [(p-t-BuC₆H₄Sb)₄(O)₂(PhPO₃)₄(PhPO₃H)₄] (C2). Based upon the initial results from docking studies, parent stibonic acids, t-butyl-phenylstibonic acid, p-isopropylphenylstibonic acid, 4-azobenzenephenylstibonic acid, and the derived tetranuclear organoantimonate-phosphonate clusters were screened against different cancer cell lines, various Gram-positive and Gram-Negative bacteria and mycobacteria for possible bioactivity profile.

Data Driven Technique for Palm Oil Mills Boiler Water Quality Monitoring in State of Perak Malaysia

Steam boilers are widely used in oil palm mills industries which physically converting the water into steam. Heating water in the boiler allows solid waste such as potassium, calcium, magnesium, silica and ions to stay in the boiler and creates a solid waste layer that coats the tubes and boilers. This strong coating would increase the heating surface of the boiler and decrease the efficiency of heat transfer. This situation will also increase the consumption of fuel, causing the boiler to overheat, which in turn leads to tube failure. The impact of this issue will cause losses to the oil palm plantation due to the shutdown of unplanned plants. Therefore, for this study, Statistical Process Control (SPC) has been utilized to monitor the quality of the 6 parameters of the boiler and boiler feedwater for palm oil mill in Perak State Malaysia to overcome the problem stated above. Boxplot has been utilized to monitor continuously the parameter based on the probability distribution of the data according to ASME guidelines. The findings from this analysis show that some of the palm oil mills is able to maintain their boiler quality accordingly, however, on controlling the silica, hardness and total iron, its need further improvement.

Anti-bacterial activity of essential oils against multidrug-resistant foodborne pathogens isolated from raw milk

The presence of pathogenic bacteria in food is considered as a primary cause of food-borne illness and food quality deterioration worldwide. The present study aimed to determine the effectiveness of five essential oils (EOs) against multidrug-resistant foodborne pathogens. In the current study Gram-negative bacteria (Escherichia, Enterobacter, Citrobacter, Proteus, Pseudomonas, and Klebsiella) and the Gram-positive bacteria Staphylococcus were isolated from raw milk and biochemically characterized. The anti-bacterial effect of different antibiotics and EOs (thyme, oregano, lemongrass, mint, and rosemary) was determined using the standard disc diffusion method. The antibiogram study revealed that Gram-negative bacteria were highly resistant to penicillin while Staphylococcus was resistant to streptomycin, amoxicillin, and lincomycin. Moderate resistance was observed to doxycycline, amikacin, enrofloxacin, kanamycin and cefixime. Isolates were found less resistant to gentamycin, chloramphenicol, and ciprofloxacin. EOs showed a broad range of antimicrobial activity against all bacteria except P. aeruginosa. Of these, thyme was more effective against most of the multi-drug resistant bacterial strains and formed the largest zone of inhibition (26 mm) against Escherichia followed by oregano oil (18 mm) against Staphylococcus (p<0.05). Klebsiella spp and Citrobacter spp showed resistance to mint and lemongrass oil respectively. The EOs such as lemongrass, mint and rosemary were less active against all the bacteria. The findings of the recent study suggest the use of EOs as natural antibacterial agents for food preservation.

A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen's useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback-Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen's work.

Comparative Study Between Two Zeolitic Imidazolate Frameworks as Adsorbents for Removal of Organoarsenic, As(III) and As(V) Species from Water

Water-stable zeolitic imidazolate frameworks (ZIF) with zinc and cobalt cations were synthesized to explore the effect of metal ions on arsenic adsorption. At room temperature (25 2 ºC) and pH 7.8, maximum adsorption capacities of arsenic (As5+) on the surface of ZIF-8 and ZIF-67 were 87.03 and 86.70 mg g-1 respectively, with encouraging results up to 95% reusability of the adsorbents. The results of this study revealed that electrostatic attraction and ion exchange were the major mechanisms responsible for better efficiencies for adsorptive removal of arsenic. The evidence for the adsorption of arsenic contaminants was confirmed by FTIR analyses. The pseudo second order and Langmuir models were best suited to explain the adsorption of arsenic species on the surface of the as-synthesized metal-organic frameworks (MOFs). Based on the results, it was possible to conclude that the metal atoms in the synthesized MOFs had a minor impact on adsorption, since these MOFs presented identical results in the removal of arsenic species. This observation can be explained by the presence of a similar organic linker (2-methyl imidazole), which points to almost the same geometry and sponginess. However, there was a slight difference in the adamsite (organo-arsenic) removal achieved by the MOFs with different metal atoms.

Damage to the human dentate gyrus impairs the perceptual discrimination of complex, novel objects

The hippocampus (HPC), and the dentate gyrus (DG) subregion in particular, is purported to be a pattern separator, orthogonally representing similar information so that distinct memories may be formed. The HPC may also be involved in complex perceptual discrimination. It is unclear if this role is limited to spatial/scene stimuli or extends to the discrimination of objects. Also unclear is whether the DG itself contributes to pattern separation beyond memory. BL, an individual with bilateral DG lesions, was previously shown to have poor discrimination of similar, everyday objects in memory. Here, we demonstrate that BL's deficit extends to complex perceptual discrimination of novel objects. Specifically, BL was presented with closely matched possible and impossible objects, which give rise to fundamentally different 3D perceptual representations despite being visually similar. BL performed significantly worse than controls when asked to select an odd object (e.g., impossible) amongst three identical counterpart objects (e.g., possible) presented at different rotations. His deficit was also evident in an atypical eye fixation pattern during this task. In contrast, BL's performance was indistinguishable from that of controls on other tasks involving the same objects, indicating that he could visually differentiate the object pairs, that he perceived the objects holistically in 3D, and that he has only a mild weakness in categorizing object possibility. Furthermore, his performance on standardized neuropsychological measures indicated intact mental rotation, visual-spatial attention, and working memory (visual and auditory). Collectively, these results provide evidence that the DG is necessary for complex perceptual discrimination of novel objects, indicating that the DG might function as a generic pattern separator of a wide range of stimuli within high-level perception, and that its role is not limited to memory.

Impact on quality of life in obstructive sialadenitis predicting outcomes after sialendoscopy

PURPOSE

Sialendoscopy is a minimally invasive procedure considered a paradigm shift in the treatment of obstructive sialadenitis. However, it shows an average need for revision procedure in up to 24% of operated cases. This study analyzed whether patient-related variables could predict the need for a revision during postoperative follow-up.

METHODS

From 2012 to 2020, this prospective comparative study analyzed demographic data as well as preoperative responses to the "Manukau Salivary Symptoms Score" (MSSS) questionnaire as predictors of the need for a revision procedure due to symptoms recurrence.

RESULTS

188 sialendoscopies (39.4% for stones/60.6% for stenoses) in 112 parotid (59.6%) and 76 submandibular glands (40.4%) were included in this study. Forty patients (21.3%) required a revision procedure. The variable "Impact on quality of life" in the preoperative period of patients with sialoliths showed that the likelihood of a revision procedure increases by 33.6% with each increase in the 10-point Likert scale presented in the MSSS (p = 0.010, OR = 1336, CI = 1.071 to 1.667). This finding was not influenced by the location of the sialolith in the duct (p = 0.415), size (p = 0.058) or number of stones (P = 0.476). Other demographic variables related to the patient showed no association with the need of a revision procedure.

CONCLUSION

Further studies should be performed to exclude the influence of other variables on the results; however, special attention should be given to patients who report a greater pre-operative impact on quality of life due to sialolithiasis.

LEVEL OF EVIDENCE

II.

Experimental analysis of excited state dynamics in Anderson-type POM@porphyrin hybrids in relevance to third-order nonlinear optical properties

This work accounts for the active contribution of life time decay in the field of nonlinear optics, especially for an energetic donor acceptor couple (porphyrin and polyoxometalate (POM)). Currently, two POM free porphyrins (Di-Tris-N@Por and Di-Tris@Por) and their two hybrids with POM (Di-Tris-NPor@Di-AndPOM-1 and Di-TrisPor@Di-AndPOM-2) have been studied keenly in nanosecond time span and resulted lifetimes ([Formula: see text]1 and [Formula: see text]2) have been compared with nonlinear optical parameters. The results demonstrated that Di-Tris-NPor@Di-AndPOM-1 exhibited better third-order nonlinear optical susceptibility [Formula: see text]3, second hyperpolarizability [Formula: see text] and nonlinear absorption [Formula: see text] and Di-TrisPor@Di-AndPOM-2 than Di-TrisPor@Di-AndPOM-2. This superiority of nonlinear optical parameters was supported by lifetime decay studies and electrochemical studies. It is revealed that more relaxation time in excited states lower will be the NLO response. The lifetime decay ([Formula: see text]1) value of Di-Tris-NPor@Di-AndPOM-1 is 3.86ns which is higher than Di-TrisPor@Di-AndPOM-2 possessing lifetime decay ([Formula: see text]1) value of 2.45ns. Moreover, lower energy of charge separated state (-0.88 eV) of Di-Tris-NPor@Di-AndPOM-1 indicates the facile electron transfer in Di-Tris-NPor@Di-AndPOM-1 than Di-TrisPor@Di-AndPOM-2 which experienced more energy of charge separated state. Rapid intersystem crossing is also responsible for the electron transfer from porphyrin moiety to POM moiety.

Post-COVID-19 Syndrome in Healthcare Personnel in Dr. Mohammad Hoesin General Hospital Palembang Indonesia

Purpose

Coronavirus infectious disease 2019 (COVID-19) had been infecting the world including healthcare personnel (HCP), but many survivors still experienced symptoms although had recovered with negative PCR results. This study aimed to identify post-COVID-19 syndrome among HCP in our hospital.

Methods & Materials

We conducted a cross-sectional study, asking HCP, surviving COVID-19 from April 2020 until February 2021, confirmed by twice negative PCR SARS CoV-2, and still working at the hospital at the time the research was conducted, to fill in an online questionnaire asking questions about symptoms related to post COVID-19 syndrome.

Results

Total study participants who completed the questionnaire were 164, 106 (65%) of them were women, consisted of 21 (13%) specialists, 52 (32%) residents, and 91 (55%) nurses. The average age was 37 (26 – 69) years old. When they experienced COVID-19, 60 (37%) participants were in asymptomatic, 76 (46%) mild, 26 (16%) moderate, and 2 (1%) severe-critical condition. Among participants, 78 (48%) still had symptoms by the time the survey was conducted, 61 (78.2%) were women, and these symptoms were still experienced in 41 (53%) survivors who had recovered more than 3 months. Fatigue was the most common symptom reported (55, 71%), followed by cough (15, 19%), joint pain (12, 15%), headache (10, 13%), muscle pain (9, 12%), breathing difficulty (7,9%), anosmia (5, 6%), bitter tongue (4, 5%).

Conclusion

Post-COVID-19 syndrome was quite common in HCP and this might cause the inability to work, treat, and care for patients optimally. Particular attention should be paid to this condition.

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time-frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

Performance of prognostic scores in prediction of 30-day postoperative mortality in COVID-19 patients after emergency surgery: A retrospective cohort study

Background

: Risk assessment with prognostic scoring, though important, is scarcely studied in emergency surgical patients with COVID-19 infection.

Methods and Material

We conducted a retrospective cohort study on adult emergency surgical patients with COVID-19 infection in our institute from 1 May 2020 to 31 October 2021 to find the 30-day postoperative mortality and predictive accuracy of prognostic scores. We assessed the demographic data, prognostic risk scores (American Society of Anesthesiologists-Physical Classification (ASA-PS), Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scores), surgical and anesthetic factors. We assessed the postoperative morbidity using the Clavien-Dindo scale and recorded the 30-day mortality. Correlation of prognostic scores and mortality was evaluated using Univariate Cox proportional hazards regression, receiver operating characteristic curve (ROC), Youden's index and Hosmer- Lemeshow goodness of fit model.

Results

Emergency surgery was performed in 67 COVID-19 patients with postoperative complication and 30-day mortality rate of 33% and 19%, respectively. A positive qSOFA and ASAPS IIIE/IVE had a 9.03- and 12.7-times higher risk of mortality compared to a negative qSOFA and ASA-PS IE/IIE (P &lt; 0.001), respectively. Every unit increase of SOFA, POSSUM and P-POSSUM scores was associated with a 50%, 18% and 17% higher risk of mortality, respectively. SOFA, POSSUM and P-POSSUM AUCROC curves showed good discrimination between survivors and non-survivors (AUC 0.8829, 0.85 and 0.86, respectively).

Conclusions

SOFA score has a higher sensitivity to predict 30-day postoperative mortality as compared to POSSUM and P-POSSUM. However, in absence of a control group of non-COVID-19 patients, actual risk attributable to COVID-19 infection could not be determined.

Aramid-wrapped CNT hybrid sol–gel sorbent for polycyclic aromatic hydrocarbons

This work describes the preparation of an analytical microextraction sorbent using a simple and versatile sol–gel hybrid composite, i.e., aramid oligomers wrapping multi-walled carbon nanotubes (CNTs) covalently bonded to a porous silica network.