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Abstract Phenotypic heterogeneity has been widely observed in cellular populations. However, the extent to which heterogeneity contains biologically or clinically important information is not well understood. Here, we investigated whether patterns of basal signaling heterogeneity, in untreated cancer cell populations, could distinguish cellular populations with different drug sensitivities. We modeled cellular heterogeneity as a mixture of stereotyped signaling states, identified based on colocalization patterns of activated signaling molecules from microscopy images. We found that patterns of heterogeneity could be used to separate the most sensitive and resistant populations to paclitaxel within a set of H460 lung cancer clones and within the NCI‐60 panel of cancer cell lines, but not for a set of less heterogeneous, immortalized noncancer human bronchial epithelial cell (HBEC) clones. Our results suggest that patterns of signaling heterogeneity, characterized as ensembles of a small number of distinct phenotypic states, can reveal functional differences among cellular populations.

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage. [ABSTRACT FROM AUTHOR]

Day-by-day increasing irrigation water scarcity requires the application of water-saving irrigation techniques to sustain agriculture production. A two-year field investigation was conducted during 2018 to 2020 to determine the effects of various mulches and irrigation volumes on the growth, leaf chemicals and soil properties of one-year-old sweet oranges (Citrus sinensis) cv. Mosambi. The study included three irrigation schedules, viz.100% ETc (I1), 80% ETc (I2), and 60% ETc (I3), and five different mulches were used, viz. without mulch, white polythene, coriander straw, dry grass and black polythene mulches, replicated thrice. Results demonstrated that drip irrigation with 100% ETc and mulching with black polythene mulch significantly increase the plant growth attributes like height of the plant (28.64%) (30.31%), rootstock girth (36.61%) (37.90%), plant canopy spread (E-W and N-S) (EW- 63.82%, NS- 63.87%) (EW- 67.56%, NS- 67.90%) and leaf area (2.4%) (2.34%). Furthermore, plant leaf chlorophyll content (2.41 mg g-1) (2.41 mg g-1) and leaf mineral content such as N (2.39%) (2.40%), P (0.16%) (0.165%), K (1.57%) (1.59%), Ca (47.34 g kg-1) (47.80 g kg-1), Mg (4.54 g kg-1) (4.57 g kg-1), Fe (120.51 g kg-1) (123.15 g kg-1) and Zn (39.00 g kg-1) (37.84 g kg-1) were noted to be significantly (p ≤ 0.05) higher in plants that received 100% (were ETc (I1) and mulching with black polythene mulch (M1) treatment. Taken together, the results suggested that treatments I1 and M1 have the potential to maximize plant growth, leaf chemicals and soil nutrients of sweet orange (Citrus sinensis) cv. Mosambi plants. [ABSTRACT FROM AUTHOR]

Automating tasks through human activity recognition in video data has become increasingly vital. Deep learning has yielded versatile activity recognition systems applicable in surveillance, healthcare analysis, sports, and human–computer interaction. Despite various proposed video-based activity recognition techniques over the years, the reliance over RGB frames, accompanied by other modalities like joint locations and depth maps, often proves less effective compared to multimodal methods. In response to this challenge, our paper introduces a competitive approach for identifying human activity in video frames. Leveraging a Convolutional Long Short-Term Memory (Conv-LSTM) network and a novel pre-processing step involving a Human Segmentation network, our method accentuates human subjects in each frame using segmentation maps. These highlighted frames undergo further processing through Convolutional Neural Networks (CNNs) to learn feature vectors, without including other modalities with RGB frames directly. The learned features are then subjected to Long Short-Term Memory (LSTM) units for comprehending sequential video data and drawing meaningful inferences. The proposed methodology undergoes rigorous testing on three publicly available datasets—KARD, MSR Daily Activity, and SBU-Interactions. Remarkably, our approach outperforms similar state-of-the-art methods, achieving benchmark accuracy scores exceeding 98% on MSR Daily Activity and 99% on KARD and SBU-Interactions datasets. In essence, our method not only provides a competitive solution for human activity recognition in video frames but also contributes to advancing the field by integrating Conv-LSTM networks and innovative pre-processing techniques. The comprehensive evaluation on multiple datasets underlines the robustness and superior performance of our proposed approach. [ABSTRACT FROM AUTHOR]

This study investigates twin screw extruded multi-walled carbon nanotube (MWCNT)–infused polyaniline (PANI) composite–based bipolar plates (BPPs) for proton exchange membrane fuel cells (PEMFCs) fabricated via fused deposition modelling (FDM). The 3D-printed composite plates with varying MWCNT proportions (5–30 wt%) were subjected to extensive characterization, including morphological study, thermal, mechanical, electrochemical corrosion, and electrical characteristics analysis. The plates with 25 wt% MWCNT (MWCNT25-PANI75) outperformed the US Department of Energy (US DoE) objectives with their high mechanical strengths exceeding 40 MPa and high thermal conductivity of 20.29 W/mK at 80 °C. Corrosion analysis showed that MWCNT25-PANI75 substantially improved corrosion resistance with a corrosion potential (Ecorr) of − 152.60 mV, a corrosion current density (Icorr) of 0.19 µA/cm2, and a protection efficiency (P.E.) of 97.29%. However, the MWCNT25-PANI75 plate is deficient in electrical properties, with an in-plane conductivity exhibited at 80.15 S/cm, which falls short of the DoE objective of 100 S/cm, demonstrating the difficulties of combining conductivity optimization with other factors. In a single-cell PEMFC system, MWCNT25-PANI75 achieved power densities of 533.91 mW/cm2, demonstrating its practicability. Further research is called for to enhance conductivity through covalent functionalization of MWCNTs, aiming to meet the US DoE targets and improve overall efficiency. [ABSTRACT FROM AUTHOR]

The temporal bone is a complex neurovascular structure. The procedure of cochlear implantation is performed with in few millimetres of area around which important structures like internal carotid artery, jugular bulb and facial nerve are present. Displacement of an electrode into the carotid canal although rare is considered as a major complication in cochlear implant surgery. The purpose of this study is to highlight the close anatomic relationship (radiologically as well as on cadaveric temporal bone dissection) between the cochlea and the carotid canal and its variations which will be helpful during cochlear implant (CI) surgery to prevent injury to internal carotid artery. This study was done in SMS Medical College and Hospital, Jaipur, India. High resolution computed tomography (HRCT) of cadaveric temporal bones was done after getting permission from ethical committee of the institute. 40 bones were studied. Cochlear carotid interval (CCI) was measured with the help of dicom reader and the same temporal bones were dissected under microscope and CCI was measured with help of millimetre scale. The CCI was found to be 2.08 ± 0.89 mm and it was ranging from 0.48 to 4.25 mm on HRCT temporal bone and on dissection 2.19 ± 0.85 mm and it was ranging from 0.85 to 4.12 mm. With the increasing popularity of CI surgeries, the importance of the CCI has become more pronounced, highlighting the need for meticulous surgical planning and technique. To the best of our knowledge this is the first study of CCI which is correlating radiological and dissection anatomy of CCI. [ABSTRACT FROM AUTHOR]

The study was aimed at determining the effectiveness of transcutaneous electrical nerve stimulation in treatment of tinnitus using Tinnitus handicap inventory (THI) and Visual analogue scale (VAS). 60 patients with bilateral, non-pulsatile, chronic subjective tinnitus; aged 18–65 years were subjected to 8 sessions (2 sessions/week) of TENS therapy given over a total period of 4 weeks. Pre-treatment and Post-treatment VAS, THI scores were compiled and analyzed statistically. A significant decrease in Tinnitus severity was seen after TENS Therapy (VAS: Loudness, Annoyance, Duration, Hampering of daily activities) (P value < 0.001). Moreover THI (Functional, Emotional & catastrophic) showed significant improvement (P value < 0.001). TENS is safe, effective, noninvasive therapeutic option for treatment of tinnitus. [ABSTRACT FROM AUTHOR]

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1–3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors. [ABSTRACT FROM AUTHOR]

The reliability of multimedia is being progressively tested by sophisticated Image Manipulation localization (IML) methods, which has led to the creation of the IML domain. A good manipulation model requires extracting non-semantic differences features between manipulated and authentic regions to exploit artifacts, which calls for explicit comparisons between the two areas. Existing models either use handcrafted-based feature methods, convolutional neural networks (CNNs), or a combination of both. Handcrafted feature methods assume the tampering beforehand, limiting their capabilities for diverse tampering operations, while CNNs model semantic information, which is not enough for the manipulation artifact. To improve these limitations, we have designed a dual-branch model that combines handcrafted feature noise and CNNs as an Encoder-decoder(ED) powered by the attention mechanism. This dual-branch model uses noise features on one branch and RGB on the other before feeding to an ED architecture for semantic learning and skip connection deployed to retain spatial information. Furthermore, this architecture uses channel spatial attention to strengthen further and refine the features' representation. Extensive experimentation on the shallowfakes dataset (CASIA, COVERAGE, COLUMBIA, NIST16) and deepfake datasets Faceforensics + + (FF + +) to demonstrate the superior feature extraction capabilities and performance to various baseline models with AUC score even reaching 99%. Also, it is one of the first methods to perform localization on the deepfake dataset. The model is relatively lighter, has 38 million parameters, and easily outperforms other State-of-the-Art(SoTA) models. [ABSTRACT FROM AUTHOR]

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022–23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day−1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day−1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model’s capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates. [ABSTRACT FROM AUTHOR]

This study demonstrated the plant growth-promoting capabilities of native actinobacterial strains obtained from different regions of the rice plant, including the rhizosphere (FT1, FTSA2, FB2, and FH7) and endosphere (EB6). We delved into the molecular mechanisms underlying the beneficial effects of these plant-microbe interactions by conducting a transcriptional analysis of a select group of key genes involved in phytohormone pathways. Through in vitro screening for various plant growth-promoting (PGP) traits, all tested isolates exhibited positive traits for indole-3-acetic acid synthesis and siderophore production, with FT1 being the sole producer of hydrogen cyanide (HCN). All isolates were identified as members of the Streptomyces genus through 16S rRNA amplification. In pot culture experiments, rice seeds inoculated with strains FB2 and FTSA2 exhibited significant increases in shoot dry mass by 7% and 34%, respectively, and total biomass by 8% and 30%, respectively. All strains led to increased leaf nitrogen levels, with FTSA2 demonstrating the highest increase (4.3%). On the contrary, strains FB2 and FT1 increased root length, root weight ratio, root volume, and root surface area, leading to higher root nitrogen content. All isolates, except for FB2, enhanced total chlorophyll and carotenoid levels. Additionally, qRT-PCR analysis supported these findings, revealing differential gene expression in auxin (OsAUX1, OsIAA1, OsYUCCA1, OsYUCCA3), gibberellin (OsGID1, OsGA20ox-1), and cytokinin (OsIPT3, OsIPT5) pathways in response to specific actinobacterial treatments. These actinobacterial strains, which enhance both aboveground and belowground crop characteristics, warrant further evaluation in field trials, either as individual strains or in consortia. This could lead to the development of commercial bioinoculants for use in integrated nutrient management practices. [ABSTRACT FROM AUTHOR]

Rice production is severely affected by various diseases such as bacterial leaf blight (BLB), brown spot (BS), false smut (FS), foot rot (FR), rice blast (RB), and sheath blight (SB). In recent years, several quantitative trait loci (QTLs) studies involving different populations have been carried out, resulting in the identification of hundreds of resistance QTLs for each disease. These QTLs can be integrated and analyzed using meta-QTL (MQTL) analysis for better understanding of the genetic architecture underlying multiple disease resistance (MDR). This study involved an MQTL analysis on 661 QTLs (378, 161, 21, 41, 44, and 16 QTLs for SB, RB, BLB, BS, FS, and FR, respectively) retrieved from 50 individual studies published from 1995 to 2021. Of these, 503 QTLs were projected finally onto the consensus map saturated with 6,275 markers, resulting in 73 MQTLs, including 27 MDR-MQTLs conferring resistance to three or more diseases. Forty-seven MQTLs were validated using marker-trait associations identified in published genome-wide association studies. A total of 3,310 genes, including both R and defense genes, were also identified within some selected high-confidence MQTL regions that were investigated further for the syntenic relationship with barley, wheat, and maize genomes. Thirty-nine high-confidence candidate genes were selected based on their expression patterns and recommended for future studies involving functional validation, genetic engineering, and gene editing. Nineteen MQTLs were co-localized with 39 known R genes for BLB and RB diseases. These results could pave the way to utilize candidate genes in a marker-assisted breeding program for MDR in rice. [ABSTRACT FROM AUTHOR]

Rare earth elements (REEs) and their oxides have become seed of the modern technology. Lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd), scandium (Sc), yttrium (Y) and their oxides La2O3, CeO2 and Y2O3 are utilized by researchers in order to enhance the microhardness, tensile strength, percentage elongation and wear resistance of the aluminum alloy composites and hybrid composites. Although aluminum boasts a lower weight-to-density ratio, high ductility, malleability and plasticity but the low strength, hardness and wear resistance limit its application. To address these shortcomings, various reinforcements have been employed. Among the available methods, powder metallurgy and stir casting are utilized by the researchers to process the aluminum metal matrix composites. The current study compiles the literature investigating the effect of rare earth elements and their oxides on mechanical properties and tribological behavior of aluminum alloys and hybrid composites. The addition of REEs and REOs to aluminum composites and hybrid composites resulted in properties suitable for applications in the aerospace, mobile phones, solar panels, fluorescent materials, marine and automobile components. [ABSTRACT FROM AUTHOR]

The gut microbiome is involved in the pathogenesis of many diseases including polycystic ovarian syndrome (PCOS). Modulating the gut microbiome can lead to eubiosis and treatment of various metabolic conditions. However, there is no proper study assessing the delivery of microbial technology for the treatment of such conditions. The present study involves the development of guar gum-pectin-based solid self-nanoemulsifying drug delivery system (S-SNEDDS) containing curcumin (CCM) and fecal microbiota extract (FME) for the treatment of PCOS. The optimized S-SNEDDS containing FME and CCM was prepared by dissolving CCM (25 mg) in an isotropic mixture consisting of Labrafil M 1944 CS, Transcutol P, and Tween-80 and solidified using lactose monohydrate, aerosil-200, guar gum, and pectin (colon-targeted CCM solid self-nanoemulsifying drug delivery system [CCM-CT-S-SNEDDS]). Pharmacokinetic and pharmacodynamic evaluation was carried out on letrozole-induced female Wistar rats. The results of pharmacokinetic studies indicated about 13.11 and 23.48-fold increase in AUC of CCM-loaded colon-targeted S-SNEDDS without FME (CCM-CT-S-SNEDDS (WFME)) and CCM-loaded colon-targeted S-SNEDDS with FME [(CCM-CT-S-SNEDDS (FME)) as compared to unprocessed CCM. The pharmacodynamic study indicated excellent recovery/reversal in the rats treated with CCM-CT-S-SNEDDS low and high dose containing FME (group 13 and group 14) in a dose-dependent manner. The developed formulation showcasing its improved bioavailability, targeted action, and therapeutic activity in ameliorating PCOS can be utilized as an adjuvant therapy for developing a dosage form, scale-up, and technology transfer. [ABSTRACT FROM AUTHOR]

Vaginal candidiasis (VC), a common recurrent gynaecological disorder affects almost 70% of women. Development of resistance to anti-fungal agents exacerbates the need for new treatments against Candida infections. The objective of the current work was to develop ethosomal vesicles (EVs) and EV hydrogel of Butea monosperma seed oil (BMSO) for treatment of vaginal candidiasis, as BMSO has demonstrated fungicidal and bactericidal activities. EVs, formulated by ethanol injection-sonication method, used Phospholipon®90G and ethanol as excipients and were optimized by Box-Behnken design (BBD). BMSO (X1), phospholipid (X2) and ethanol concentration (X3) were selected as input variables, while vesicle size (Y1), polydispersity index (PDI) (Y2) and percent entrapment efficiency (%EE) (Y3) were taken as response variables. The size of optimized EVs determined by dynamic light scattering technique was found to be 249 ± 2.6 nm; PDI was 0.301 ± 0.013; %EE determined by high-speed centrifugation method was 53.84 ± 1.98%. Field emission scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HR-TEM) unveiled vesicles of rhombic dodecahedral shape with smooth, sealed structure. The EVs, incorporated into carbopol gel, demonstrated non-Newtonian behaviour on a rotary type rheometer. Minimal inhibitory concentration (MIC) determination by broth microdilution method revealed MIC of EVs and hydrogel to be 2.5 times less, as compared to BMSO against Candida albicans (C.albicans). Anti-candidal activities determined using agar well diffusion method demonstrated better zone of inhibition for hydrogel vis-a-vis BMSO. Pharmacodynamic evaluation conducted on Balb/c female mice manifested a comparable decrease in fungal burden by EVs and EV hydrogel to marketed clotrimazole. Histopathology of vaginal tissue of treated groups showed no or mild inflammation with normal skin structure. The results disclosed EVs and EV hydrogel to possess better anti-fungal activity vis-a-vis BMSO and can be explored as potential alternative herbal product for VC. [ABSTRACT FROM AUTHOR]

The increasing adoption of deep learning algorithms for automating downstream natural language processing (NLP) tasks has created a need to enhance their capability to assess linguistic acceptability. The CoLA corpus was created to aid in the development of models that can accurately assess grammatical acceptability and evaluate linguistic proficiency. Transformer models, widely utilized in various natural language processing tasks, including the evaluation of linguistic acceptability, may possess limitations that undermine their perceived robustness. These models exhibit susceptibility to adversarial text attacks, which are characterized by inconspicuous modifications made to the original input text. The tactfully chosen modifications are such that the adversarial examples generated, although correctly classified by human observers, successfully mislead the targeted model of the attack, consequently hindering its reliability. This paper presents a novel framework called ‘Homograph’ to generate adversarial text in a black-box setting. The efficacy of the suggested attack in undermining models designed for linguistic acceptability is significantly enhanced by its capability to generate visually similar adversarial examples that do not compromise the grammatical acceptability of the original input samples. These examples effectively deceive the model, causing it to modify its predicted label. In the context of the linguistic acceptability task, our attack was effectively applied to five transformer models: ALBERT, BERT, DistilBERT, RoBERTa, and XL-Net, fine-tuned on the CoLA dataset. Our work distinguishes itself from existing text-based attacks through several contributions. Firstly, we surpass previous baselines in terms of attack success rate ( ASR ) and average perturbation rate ( APR ) for models trained on the CoLA dataset. Secondly, we generate more potent adversarial examples that contain imperceptible modifications, thereby preserving the original label. Lastly, we employ a straightforward character-level transformation technique to produce adversarial examples that closely resemble the original text. [ABSTRACT FROM AUTHOR]

Eustachian tube is complex and inaccessible structure. It connects nasopharynx to middle ear. It is composed of cartilaginous and bony segments. Eustachian tube helps in pressure regulation, protection and clearance of the middle ear. Dysfunctions of Eustachian tube are either due to anatomical obstruction or functional failure. Whether variation of length, width of bony canal, isthmus, length of the cartilaginous canal and angulation of the Eustachian tube to horizontal plane affects overall pressure regulation, protection and mucociliary clearance leads to the spread of the infection to the middle ear are highlighted. A comparative prospective study design of 20 adults of both gender of normal ear computerized tomography done for some other cause and 20 adults of both gender with chronic otitis media (tubo-tympanic pathology). After obtaining consent, clinical examination, all patients had got computerized tomography study of the temporal bone and were evaluated to study the variation of length, width of bony canal, isthmus, length of the cartilaginous canal and angulation of the Eustachian tube to horizontal plane. In our series cartilaginous Eustachian tube length is 26.72 mm in normal ear and 26.17 mm in diseased ear. Long and reduced diameter of bony segment is the anatomical cause in the tubotympanic pathology along with inflammation and loss of ciliary moment. Normal ear bony width is 2.36 mm; isthmus 1.18 mm are comparatively more than diseased ear (bony width 2.13 & isthumus1.04). Whereas bony length of normal ear is 11.21 mm and diseased ear is 11.62 mm. Decreased Eustachian tube pretympanic diameter and Reid –plane ET angle can be used to predict Eustachian tube dysfunction. Angle to horizontal plane in our series in normal ear 44.670 whereas in diseased ear 45.10. The Eustachian tube plays an important role in the development of the tubotympanic pathology of the ear. Pre-existing Eustachian tube anatomical variation appears to be risk factor in developing tubotympanic pathology. [ABSTRACT FROM AUTHOR]

We report a rare case of multiple benign minor salivary gland tumors in a 60-year-old woman, presenting with progressive left nasal obstruction, intermittent anterior nasal discharge, and postnasal drip. Diagnostic nasal endoscopy and computed tomography (CT) scan revealed sessile, pale, non-pulsating masses in the left nasal cavity, septum, and nasopharynx. Histopathological examination confirmed ectopic minor salivary glands. The tumors were successfully removed via endoscopic excision, and the patient experienced resolution of symptoms with no evidence of recurrence during follow-up. This case highlights the importance of a systematic approach involving clinical assessment, imaging, and histopathological analysis for accurate diagnosis and management of nasal salivary masses. [ABSTRACT FROM AUTHOR]

Mg-0.5Ca alloy is a potential candidate for biodegradable implants. The addition of 2 wt pct Zn and 0.5 wt pct Sn was shown to improve the degradation resistance in Hank's balanced salt solution (HBSS) at room temperature. However, the alloy exhibited excessive pitting due to the formation of numerous second phase precipitates namely Mg2Zn11 and Mg2Sn. Hence, T4 heat treatment was performed that resulted in complete dissolution of the Mg2Sn phase. This resulted in a significant reduction in the volta-potential difference between the matrix and Mg2Zn11 phase of the T4-treated alloy, as confirmed by scanning Kelvin probe force microscopy (SKPFM). The T4-treated alloy encountered less micro-galvanic attack during immersion periods in simulated body fluid owing to the smaller grains, reduced volume fraction of second phases and decreased potential difference between the phases. As a result, the T4-treated XZT020 alloy demonstrated improved corrosion resistance with fewer and smaller shallower pits on the corroded surface than the as-cast XZT020 alloy. Mg2O and Mg(OH)2 are the corrosion byproducts identified on both alloy surfaces, according to XRD investigation of the corroded surface. [ABSTRACT FROM AUTHOR]

This article introduces an effective numerical method for solving a weakly coupled system of singularly perturbed delay differential equations. The approach involves approximating the first-order derivative in the system using a Taylor series, followed by the application of finite difference technique. A scheme that incorporates a fitting factor is developed for problem resolution. To assess the accuracy of the method, error estimation is conducted using the maximum principle, and theoretical justifications are supported by relevant numerical examples involving varying perturbation parameters and mesh sizes. The numerical results are presented in terms of maximum absolute errors and the rate of convergence. Our approach is demonstrated to be first order uniformly convergent, as evident from the tabulated values. In comparison to previously published methodologies, the present approach offers improved results and contributes to the existing literature. [ABSTRACT FROM AUTHOR]

The environmentally friendly disposal of copper waste from automobile shops is crucial for preventing potential environmental hazards. This study introduces an innovative approach to repurpose retrieved copper by converting it into a nanomaterial, specifically a copper oxide-based nanoparticles (CSCNC). Using extracts derived from Curcuma amada leaves and Strychnos potatorum seeds, a green synthesis process were employed to synthesize CSCNCs. The synthesis involved dissolving the reduced copper waste, leaching copper into the solution, and precipitating nanoparticles using a pH-controlled method. The synthesized CSCNCs possess a monoclinic crystalline structure with a morphology resembling nano-leaf sheets, exhibiting an average pore diameter of 14 nm and a mesoporous structure. These CSCNCs demonstrated efficient degradation of Congo red (CR), a representative dye, under visible light irradiation, with a degradation efficiency of 91% under optimized conditions. Kinetic analysis revealed that pseudo-second-order reactions provided a better fit for the degradation process, with CSCNCs exhibiting exceptional performance. Radical trapping experiments identified hydroxyl radicals and holes as the primary species involved in the photocatalytic degradation mechanism. The development of CSCNCs represents a significant advancement in the repurposing of copper waste, contributing to environmental sustainability and protection while offering insights into potential applications in wastewater treatment. [ABSTRACT FROM AUTHOR]

The vast majority of online media rely heavily on the revenues generated by their readers' views, and due to the abundance of such outlets, they must compete for reader attention. It is a common practise for publishers to employ attention-grabbing headlines as a means to entice users to visit their websites. These headlines, commonly referred to as clickbaits, strategically leverage the curiosity gap experienced by users, enticing them to click on hyperlinks that frequently fail to meet their expectations. Therefore, the identification of clickbaits is a significant NLP application. Previous studies have demonstrated that language models can effectively detect clickbaits. Deep learning models have attained great success in text-based assignments, but these are vulnerable to adversarial modifications. These attacks involve making undetectable alterations to a small number of words or characters in order to create a deceptive text that misleads the machine into making incorrect predictions. The present work introduces "Non-Alpha-Num", a newly proposed textual adversarial assault that functions in a black box setting, operating at the character level. The primary goal is to manipulate a certain NLP model in a manner that the alterations made to the input data are undetectable by human observers. A series of comprehensive tests were conducted to evaluate the efficacy of the suggested attack approach on several widely-used models, including Word-CNN, BERT, DistilBERT, ALBERTA, RoBERTa, and XLNet. These models were fine-tuned using the clickbait dataset, which is commonly employed for clickbait detection purposes. The empirical evidence suggests that the attack model being offered routinely achieves much higher attack success rates (ASR) and produces high-quality adversarial instances in comparison to traditional adversarial manipulations. The findings suggest that the clickbait detection system has the potential to be circumvented, which might have significant implications for current policy efforts. [ABSTRACT FROM AUTHOR]

In recent years, artificial faces generated using Generative Adversarial Networks (GANs) and Variational Auto-encoders (VAEs) have become more lifelike and difficult for humans to distinguish. Deepfake refers to highly realistic and impressive media generated using deep learning technology. Convolutional Neural Networks (CNNs) have demonstrated significant potential in computer vision applications, particularly identifying fraudulent faces. However, if these networks are trained on insufficient data, they cannot effectively apply their knowledge to unfamiliar datasets, as they are susceptible to inherent biases in their learning process, such as translation, equivariance, and localization. The attention mechanism of vision transformers has effectively resolved these limits, leading to their growing popularity in recent years. This work introduces a novel module for extracting global texture information and a model that combines data from CNN (ResNet-18) and cross-attention vision transformers. The model takes in input and generates the global texture by utilizing Gram matrices and local binary patterns at each down sampling step of the ResNet-18 architecture. The ResNet-18 main branch and global texture module operate simultaneously before inputting into the visual transformer’s dual branch’s cross-attention mechanism. Initially, the empirical investigation demonstrates that counterfeit images typically display more uniform textures that are inconsistent across long distances. The model’s performance on the cross-forgery dataset is demonstrated by experiments conducted on various types of GAN images and Faceforensics + + categories. The results show that the model outperforms the scores of many state-of-the-art techniques, achieving an accuracy score of up to 85%. Furthermore, multiple tests are performed on different data samples (FF + +, DFDCPreview, Celeb-Df) that undergo post-processing techniques, including compression, noise addition, and blurring. These studies validate that the model acquires the shared distinguishing characteristics (global texture) that persist across different types of fake picture distributions, and the outcomes of these trials demonstrate that the model is resilient and can be used in many scenarios. [ABSTRACT FROM AUTHOR]

Phytohormone levels are regulated through specialized enzymes, participating not only in their biosynthesis but also in post-signaling processes for signal inactivation and cue depletion. Arabidopsis thaliana (At) carboxylesterase 15 (CXE15) and carboxylesterase 20 (CXE20) have been shown to deplete strigolactones (SLs) that coordinate various growth and developmental processes and function as signaling molecules in the rhizosphere. Here, we elucidate the X-ray crystal structures of AtCXE15 (both apo and SL intermediate bound) and AtCXE20, revealing insights into the mechanisms of SL binding and catabolism. The N-terminal regions of CXE15 and CXE20 exhibit distinct secondary structures, with CXE15 characterized by an alpha helix and CXE20 by an alpha/beta fold. These structural differences play pivotal roles in regulating variable SL hydrolysis rates. Our findings, both in vitro and in planta, indicate that a transition of the N-terminal helix domain of CXE15 between open and closed forms facilitates robust SL hydrolysis. The results not only illuminate the distinctive process of phytohormone breakdown but also uncover a molecular architecture and mode of plasticity within a specific class of carboxylesterases.Plant hormone levels are regulated by synthesis and breakdown, but the mechanism of strigolactone degradation remains unclear. This study reveals the structure of distinct carboxylesterases, which switch between open and closed conformations to facilitate strigolactone catabolism in plants. [ABSTRACT FROM AUTHOR]

A series of novel lead free tungsten bronze (T-B) structured Ba4(La2-xBix)Fe2Nb8O30(x = 0, 0.05, 0.1, 0.15, 0.2, and 0.25) were formulated by traditional high temperature solid-state method and calcined at 1200 °C. The structural property was investigated by X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. Tetragonal structure was confirmed from Rietveld analysis. The scanning electron micrograph (SEM) shows uniform distribution of rod-shaped grains with less porosity. The UV–vis analysis shows that the materials have band gaps in the range 2.17–2.29 eV which makes them useful for photocatalytic applications. The dielectric measurement of the samples was carried out in a broad range of frequency as well as temperature. The impedance of the materials falling off with the rise in temperature showing their negative temperature coefficient (NTCR) behavior. The variation of polarization with electric field confirms the ferroelectric nature of the samples. [ABSTRACT FROM AUTHOR]

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%. [ABSTRACT FROM AUTHOR]

Key message: Unraveling genetic markers for MYMIV resistance in urdbean, with 8 high-confidence marker-trait associations identified across diverse environments, provides crucial insights for combating MYMIV disease, informing future breeding strategies. Globally, yellow mosaic disease (YMD) causes significant yield losses, reaching up to 100% in favorable environments within major urdbean cultivating regions. The introgression of genomic regions conferring resistance into urdbean cultivars is crucial for combating YMD, including resistance against mungbean yellow mosaic India virus (MYMIV). To uncover the genetic basis of MYMIV resistance, we conducted a genome-wide association study (GWAS) using three multi-locus models in 100 diverse urdbean genotypes cultivated across six individual and two combined environments. Leveraging 4538 high-quality single nucleotide polymorphism (SNP) markers, we identified 28 unique significant marker-trait associations (MTAs) for MYMIV resistance, with 8 MTAs considered of high confidence due to detection across multiple GWAS models and/or environments. Notably, 4 out of 28 MTAs were found in proximity to previously reported genomic regions associated with MYMIV resistance in urdbean and mungbean, strengthening our findings and indicating consistent genomic regions for MYMIV resistance. Among the eight highly significant MTAs, one localized on chromosome 6 adjacent to previously identified quantitative trait loci for MYMIV resistance, while the remaining seven were novel. These MTAs contain several genes implicated in disease resistance, including four common ones consistently found across all eight MTAs: receptor-like serine-threonine kinases, E3 ubiquitin-protein ligase, pentatricopeptide repeat, and ankyrin repeats. Previous studies have linked these genes to defense against viral infections across different crops, suggesting their potential for further basic research involving cloning and utilization in breeding programs. This study represents the first GWAS investigation aimed at identifying resistance against MYMIV in urdbean germplasm. [ABSTRACT FROM AUTHOR]

In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria. [ABSTRACT FROM AUTHOR]

Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions. [ABSTRACT FROM AUTHOR]

Al-Si alloy as a matrix in Aluminium Metal Matrix Composites (AMCs) augments the hardness and strength. The work reports the characterization and mechanical properties of Hybrid Aluminium Metal Matrix Nanocomposites (HAMNCs) using Hypereutectic Al-Si grade LM30 alloy as the matrix with boron Carbide (nB4C) and Rutile (nTiO2) nanoparticles as reinforcements. Liquid metallurgy route ultrasonic squeeze-assisted stir-casting was employed to manufacture hybrid formulations. The HAMNC specimens were fabricated by maintaining 1 wt.% of nB4C and by varying TiO2 nanoparticles in steps of 0.25 wt.% in the 0 to 1 wt.% range. The optical micrographs revealed that ultrasonic vibration and squeeze casting effects aided the uniform distribution of nano reinforcements and lowered the porosity. XRD analysis revealed the formation of Aluminum Titanium Silicate (Al4Ti2SiO12) due to the addition of nTiO2. The results highlighted that the LM30 matrix having 1.0 wt.% of nB4C and 0.75 wt.% of nTiO2 exhibited the maximum hardness of 107.2 HRB, ultimate tensile strength of 265.52 MPa, and compressive strength of 656 MPa. The tensile fractography using FESEM highlighted the various kinds of dendrites associated with the fracture. nB4C and TiO2 nano reinforcements increased energy absorption during impact failure. [ABSTRACT FROM AUTHOR]

Polar codes are the popular error-correcting codes and increased their attention after being adopted for the control channel in fifth-generation new radio (5G NR) standards. An efficient hardware architecture for polar code is often required with minimal encoding and decoding complexity. This work proposes a Multi-folded pipelined architecture and analyzes the performance in terms of latency, hardware utilization, and throughput. The designed architecture has two folded architectures interconnected in parallel to output 4-bits simultaneously. Folding transformations are used to reduce the number of idle processing elements (PEs) in every stage leading to the effective utilization of PE. Precomputation is effectively utilized in the PE to reduce the critical path delay, which improves the maximum operating frequency. A Loop-based shifting register (LSR) is employed to reduce the number of registers used. The analytical model for latency and utilization rate has been derived from the scheduling of the proposed architecture. The proposed design shows 63–71% higher hardware utilization than conventional semi-parallel design for code length N = 512 suitable for the physical downlink control channel (PDCCH) in 5G NR. The architecture is also implemented in Virtex-6, ZYNQ-Ultrascale+ MPSoC device for maximum supported code length of 5G NR, i.e., up to 2 10 , compared with the existing decoders. The proposed design also has the benefit of lesser look-up-table (LUT) consumption and zero random-access-memory (RAM) usage with some additional registers, making it suitable for resource-constraint applications. [ABSTRACT FROM AUTHOR]

Accurate forecasting of daily evapotranspiration (ET) is essential for enhancing real-time irrigation scheduling and informed decision-making in water resources allocation. This study investigates the intricate relationships between meteorological variables and evapotranspiration (ET) to enhance the accuracy of ET estimation models. Robust correlations were identified, emphasizing the significance of net radiation (Rn) in predicting ET. The study explores three distinct scenarios, incorporating different combinations of weather variables as input. The first scenario incorporates all weather variables, including date and time, as inputs for model development. The second scenario utilizes only Rn as input to predict ET values. In the third and final scenario, all weather variables, along with date and time, are employed as inputs for comprehensive model development. The multivariate linear regression (MLR) model demonstrated exceptional performance when exclusively using Rn, achieving an impressive R2 value of 0.99 in both calibration and validation phases. However, limitations were observed when Rn was excluded, highlighting the necessity of a comprehensive set of input data. Penalized regression models, including ridge regression, LASSO, and ELNET, exhibited improved performance with the inclusion of Rn, supporting the importance of this variable in refining ET estimates. Machine learning models displayed remarkable performance, with most achieving R2 values exceeding 0.95 in scenarios involving extensive input data. The Support Vector Regression (SVR) model faced challenges, indicating potential overfitting in certain scenarios. In scenarios with limited input data, machine learning models exhibited varying performance, with the Random Forest (RF) model emerging as the most robust model with R2 value of 0.99 and 0.84 during the calibration and validation, respectively. [ABSTRACT FROM AUTHOR]

Hydrological data is crucial for accurate forecasting of precipitation which can be used for water resources planning and management. The purpose of this study is to develop a seasonal rainfall forecast model, using a hybrid wavelet-artificial neural network (WANN) model based on regression analysis to predict seasonal rainfall in Almora, Lansdown, Kashipur and Mukteswar region in Uttarakhand (India).The statistical results shows that the mean maximum rainfall was found to be 746.82 mm, 1586.58 mm, 1060.53 mm and 964.43 mm for Almora, Lansdown, Kashipur and Mukteswar, respectively. The models WANN-03 (Network 4–8-1), WANN-10 (Network 4–7-1), WANN-10 (Network 4–7-1) and WANN-15 (Network 4–8-1) were found to be the most efficient models for Mukteswar, Lansdown, Kashipur and Almora, based on the high coefficient of determination (R2) and coefficient of efficiency (CE) values and low root mean square error (RMSE) values that were obtained using each model. For each season, four WANN modelshave been developed (total of sixteen models) by varying the number of hidden neurons. The results shows that only one WANN model was not sufficient to predict the rainfall of all stations. Every station has a specific networked model which could model the data more precisely preciously. The findings illustrated that the hybrid model of WANN having Network (4–7-1) was found most superior model (R2 = 0.857, RMSE = 32.192 and CE = 0.846) for the Lansdown stations among all the stations. [ABSTRACT FROM AUTHOR]

In the industrial revolution 4.0 the applicability of cyber physical systems (CPS) extensively increased in various sectors including logistics, health, and manufacturing. The integration of CPS in logistics has revolutionized the way goods are transported, managed, and monitored. CPS combines digital and physical components to enhance the efficiency and effectiveness of logistics operations. However, to ensure the reliable and continuous operation of CPS, optimizing the availability of its subsystems is paramount. Hence, in present study, an effort is made to explore the applicability of metaheuristic approaches in the performance evaluation of cyber physical systems and a comprehensive evaluation framework is also proposed to compare the performance of metaheuristic algorithms. For this purpose, a Markov model of cyber physical system developed by considering constant failure and repair rates for all components. The failure and repair rates are followed exponential distribution. The concept of cold standby redundancy is utilized for two components including analog components and sensors & actuators unit. All components of cyber physical system can face failure during the working process and are perfectly repairable. The mathematical expression of system is derived and optimized by using metaheuristic approaches namely grey wolf optimization (GWO), cuckoo search algorithm (CS), dragonfly optimization (DA), grasshopper optimization algorithm (GOA) and cat swarm optimization (CSO). The numerical expression of the system availability obtained at various population sizes and estimated parameters derived. It is revealed from the numerical investigation that GWO and CSO outperform all optimization techniques. GWO attains its maximum availability 0.9983290 at population size 140 over 500 iterations and CSO attains its maximum availability 0.9983297 at population size 120 over 1000 iterations. The results of present study provide valuable insights into the strengths and weaknesses of each metaheuristic approach in the context of CPS subsystem optimization. This comparative analysis can guide logistics professionals and researchers in selecting the most suitable optimization algorithm for their specific CPS applications. [ABSTRACT FROM AUTHOR]

Marine macroalgae (seaweeds) have been attracting the attention of both consumers and researchers in recent days because of their enormous nutritional and biotechnological properties. Seaweeds are autotrophic plants that are edible in Asia, especially in Indonesia, Philippines, China, and Korea, and recently in Europe, the USA, and Australia. The high rate of lipids, proteins, and fiber-content of seaweeds is an advantageous one. Their bioactive molecules play a major role in the pharmaceutical, nutraceutical, and cosmetics industries. The daily usage of seaweed has been increasing day by day, but their wild harvest rate does not meet their requirements. Hence, on-shore seaweed cultivation was initiated several decades ago in some Asian countries, and subsequently such culture-methods have been followed in some other countries (to encourage seaweed cultivation and its associated products). On-shore cultivation would provide a large algal-quantity at a specific time interval, but when it comes to edible purposes, on-shore cultivation fails to provide contamination free, and nutritionally rich seaweeds. Therefore, the land-based tank cultivation-practise could provide an opportunity to cultivate seaweed (without contamination) and also to make it possible to enhance their nutritional compounds under laboratory conditions initially. Natural calamities may not affect the cultivation of seaweed in tanks. It would ensure the continuous supply of raw material to consumers and industries. This review discusses the world scenario of edible seaweed production especially the green seaweeds, as to why land-based seaweed cultivation is essential, the advantages of tank cultivation, how to select the seaweeds for tank cultivation, the methods of plantlet preparation and the indoor and the outdoor cultivation in tanks, basic applications of tank cultivated seaweeds; and the challenges during tank cultivation. [ABSTRACT FROM AUTHOR]

Asthma, lung cancer, cystic fibrosis, tuberculosis, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and COVID-19 are few examples of inflammatory lung conditions that cause cytokine release syndrome. It can initiate a widespread inflammatory response and may activate several inflammatory pathways that cause multiple organ failures leading to increased number of deaths and increased prevalence rates around the world. Nanotechnology-based therapeutic modalities such as nanoparticles, liposomes, nanosuspension, monoclonal antibodies, and vaccines can be used in the effective treatment of inflammatory lung diseases at both cellular and molecular levels. This would also help significantly in the reduction of patient mortality. Therefore, nanotechnology could be a potent platform for repurposing current medications in the treatment of inflammatory lung diseases. The aim and approach of this article are to highlight the clinical manifestations of cytokine storm in inflammatory lung diseases along with the advances and potential applications of nanotechnology-based therapeutics in the management of cytokine storm. Further in-depth studies are required to understand the molecular pathophysiology, and how nanotechnology-based therapeutics can help to effectively combat this problem. [ABSTRACT FROM AUTHOR]

Moringa oleifera Lam. is a pan-tropical plant well known to the ancient world for its extensive therapeutic benefits in the Ayurvedic and Unani medical systems. The ancient world was familiar with this tree, but it has only lately been rediscovered as a multifunctional species with a huge range of possible therapeutic applications. It is a folk remedy for skin diseases, edema, sore gums, etc. This review comprises the history, ethnomedicinal applications, botanical characteristics, geographic distribution, propagation, nutritional and phytochemical profile, dermatological effects, and commercially available cosmeceuticals of Moringa oleifera Lam. Compilation of all the presented data has been done by employing various search engines like Science Direct, Google, PubMed, Research Gate, EBSCO, SciVal, SCOPUS, and Google Scholar. Studies on phytochemistry claim the presence of a variety of substances, including fatty acids, phenolic acids, sterols, oxalates, tocopherols, carotenoids, flavonoids, flavonols glycosides, tannins, terpenoids, terpene, saponins, phylates, alkaloids, glucosinolates, glycosides, and isothiocyanate. The pharmacological studies have shown the efficacy of Moringa oleifera Lam. as an antibacterial, antifungal, anti-inflammatory, antioxidant, anti-atopic dermatitis, antipsoriatic, promoter of wound healing, effective in treating herpes simplex virus, photoprotective, and UV protective. As a moisturizer, conditioner, hair growth promoter, cleanser, antiwrinkle, anti-aging, anti-acne, scar removal, pigmentation, and control for skin infection, sores, as well as sweating, it has also been utilized in a range of cosmeceuticals. he Moringa oleifera Lam. due to its broad range of phytochemicals can be proven boon for the treatment of dermatological disorders. [ABSTRACT FROM AUTHOR]

The aim of this review is to assess the impact of cell phone radiation effects on green plants. Rapid progress in networking and communication systems has introduced frequency- and amplitude-modulated technologies to the world with higher allowed bands and greater speed by using high-powered radio generators, which facilitate high definition connectivity, rapid transfer of larger data files, and quick multiple accesses. These cause frequent exposure of cellular radiation to the biological world from a number of sources. Key factors like a range of frequencies, time durations, power densities, and electric fields were found to have differential impacts on the growth and development of green plants. As far as the effects on green plants are concerned in this review, alterations in their morphological characteristics like overall growth, canopy density, and pigmentation to physiological variations like chlorophyll fluorescence and change in membrane potential etc. have been found to be affected by cellular radiation. On the other hand, elevated oxidative status of the cell, macromolecular damage, and lipid peroxidation have been found frequently. On the chromosomal level, micronuclei formation, spindle detachments, and increased mitotic indexes etc. have been noticed. Transcription factors were found to be overexpressed in many cases due to the cellular radiation impact, which shows effects at the molecular level. [ABSTRACT FROM AUTHOR]

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image-splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents twofold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel spatio-compression lightweight splice detection framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it 'information preserving.' Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively. Hence, this study proposes modified Blackman bandpass filter—greedy particle swarm optimization (MBBF-GPSO) with convolutional neural network (CNN) for effective seizure detection. In this case, unwanted signals (noise) is eliminated by MBBF as it possess better ability in stopband attenuation, and, only the optimized features are selected using GPSO. For enhancing the efficacy of obtaining optimal solutions in GPSO, the time and frequency domain is extracted to complement it. Through this process, an optimized features are attained by MBBF-GPSO. Then, the CNN layer is employed for obtaining the productive classification output using the objective function. Here, CNN is employed due to its ability in automatically learning distinct features for individual class. Such advantages of the proposed system have made it explore better performance in seizure detection that is confirmed through performance and comparative analysis. [ABSTRACT FROM AUTHOR]

Environmentally, biodegradable natural fiber plays a vital role in polymer composite fabrication due to its cost-effectiveness, reduced specific weight, and specific stiffness behavior. Besides, the research gap spotted on processing challenges, including lack of adhesive behavior, results in lower mechanical behavior of composite. The main objective of the present research is synthesizing of epoxy-based poly composite enclosures with 5% sodium hydroxide processed natural sisal fiber (SF) as 20 vol% via thermally assisted injection mold route, which functional behavior is enriched by the accumulations of 3, 6, and 9 vol% of nanosilicon carbide particles (SiC). The impacts of processing accumulations of nano-SiC/SF on metallurgical (scanning electron microscope (SEM)) and mechanical functional properties including tensile strength, elongation percentage, flexural strength, and fracture toughness of epoxy/NaOH-treated natural SF and its composites embedded with nano-SiC are studied. The novel research is recorded with the exposure of SEM metallurgical analysis results, and better SF distribution along the epoxy matrix with a coarse grain of nano-SiC is identified on hybrid compositions. Amid the various compositions mentioned above, the epoxy/20 vol% SF (NaOH treated)/6 vol% nano-SiC composite has superior tensile strength, better elongation percentage, improved flexural strength, and good fracture toughness of 54 ± 1.5 MPa, 18 ± 1%, 76 ± 2 MPa, and 1.61 ± 0.04 MPam0.5, which is greater than the value of epoxy/20 vol% SF (NaOH treated) composite sample. [ABSTRACT FROM AUTHOR]

Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e., Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are among the leading causes of mortality worldwide. Cigarette smoking is among the main aetiologic factors for both ailments. These diseases share common pathogenetic mechanisms including inflammation, oxidative stress, and tissue remodelling. Current therapeutic approaches are limited by low efficacy and adverse effects. Consequentially, LC has a 5-year survival of < 20%, while COPD is incurable, underlining the necessity for innovative treatment strategies. Two promising emerging classes of therapy against these diseases include plant-derived molecules (phytoceuticals) and nucleic acid-based therapies. The clinical application of both is limited by issues including poor solubility, poor permeability, and, in the case of nucleic acids, susceptibility to enzymatic degradation, large size, and electrostatic charge density. Nanoparticle-based advanced drug delivery systems are currently being explored as flexible systems allowing to overcome these limitations. In this review, an updated summary of the most recent studies using nanoparticle-based advanced drug delivery systems to improve the delivery of nucleic acids and phytoceuticals for the treatment of LC and COPD is provided. This review highlights the enormous relevance of these delivery systems as tools that are set to facilitate the clinical application of novel categories of therapeutics with poor pharmacokinetic properties. This picture was generated with BioRender [ABSTRACT FROM AUTHOR]

Recently, in human and animal diseases, inflammation and pain have become the focus of scientific research as it affects more people than any other disease. Inflammation is the natural phenomenon of the immune response to remove stimuli when the body is exposed to external/internal injury. Eulophia nuda Lindl. is a perennial orchid and belongs to the family Orchidaceae. This plant is used according to folklore for the treatment of human breast cancer, inflammation, pain and as an antioxidant. The present study aims to evaluate the anti-inflammatory and analgesic potential of the ethanolic extract of tubers and different fractions of Eulophia nuda. The ethanolic extract (ENEE) and different fractions of E. nuda were prepared by liquid-liquid extraction. ENEE and obtained fractions were used to evaluate anti-inflammatory activity assessed on carrageenan-induced inflammation using the cotton pellet method. The ENEE and different fractions were also used to evaluate analgesic activity by assessing via the tail immersion test and hot plate method. The significant results from the present study indicate that ethanolic extract and its different fractions possess anti-inflammatory and analgesic potential, supporting the traditional uses of the plant. Further investigation is required to reveal the exact metabolites responsible for the observed result. [ABSTRACT FROM AUTHOR]

Parkinson's disease is the second most common neurodegenerative condition with its prevalence projected to 8.9 million individuals globally in the year 2019. Parkinson's disease affects both motor and certain non-motor functions of an individual. Numerous research has focused on the neuroprotective effect of the glial cell line–derived neurotrophic factor (GDNF) in Parkinson's disease. Discovered in 1993, GDNF is a neurotrophic factor identified from the glial cells which was found to have selective effects on promoting survival and regeneration of certain populations of neurons including the dopaminergic nigrostriatal pathway. Given this property, recent studies have focused on the exogenous administration of GDNF for relieving Parkinson's disease–related symptoms both at a pre-clinical and a clinical level. This review will focus on enumerating the molecular connection between Parkinson's disease and GDNF and shed light on all the available drug delivery approaches to facilitate the selective delivery of GDNF into the brain paving the way as a potential therapeutic candidate for Parkinson's disease in the future. [ABSTRACT FROM AUTHOR]

Since the layer of the Earth is non-homogeneous and initially stressed, this paper has framed to discuss propagation of torsional type surface wave in an initially stressed poroelastic medium sandwiched between a non-homogeneous layer with linear type variation in density and rigidity and a half-space of heterogeneous medium with exponential type variation in density and rigidity. Dispersion equation of this kind of wave has been obtained with the help of Whittaker's expansion and separation of variables method under some specified boundary conditions. The effect of non-homogeneity related with layer and half-space and initial stress of sandwiched medium have been illustrated based on their graphical discussion. It has been found that the presence of non-homogeneity in the material enhances the phase velocity but it also enhances the phase velocity with descending values of initial stress. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the potential of zerumbone-loaded liquid crystalline nanoparticles (ZER-LCNs) in the protection of broncho-epithelial cells and alveolar macrophages against oxidative stress, inflammation and senescence induced by cigarette smoke extract in vitro. The effect of the treatment of ZER-LCNs on in vitro cell models of cigarette smoke extract (CSE)-treated mouse RAW264.7 and human BCi-NS1.1 basal epithelial cell lines was evaluated for their anti-inflammatory, antioxidant and anti-senescence activities using colorimetric and fluorescence-based assays, fluorescence imaging, RT-qPCR and proteome profiler kit. The ZER-LCNs successfully reduced the expression of pro-inflammatory markers including Il-6, Il-1β and Tnf-α, as well as the production of nitric oxide in RAW 264.7 cells. Additionally, ZER-LCNs successfully inhibited oxidative stress through reduction of reactive oxygen species (ROS) levels and regulation of genes, namely GPX2 and GCLC in BCi-NS1.1 cells. Anti-senescence activity of ZER-LCNs was also observed in BCi-NS1.1 cells, with significant reductions in the expression of SIRT1, CDKN1A and CDKN2A. This study demonstrates strong in vitro anti-inflammatory, antioxidative and anti-senescence activities of ZER-LCNs paving the path for this formulation to be translated into a promising therapeutic agent for chronic respiratory inflammatory conditions including COPD and asthma. [ABSTRACT FROM AUTHOR]