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BackgroundSoil salinity is a real challenge in nowadays crop production in many regions. Various strategies have been applied to increase plant salinity tolerance. Salicylic acid (SA) frequently has been reported to increase plant salinity tolerance; however, the comparative efficiency of soil (root) or foliar application of SA has not been well tested yet. In this study, the effects of root or leaf pretreatment, and leaf treatment with 100 mg L−1 salicylic acid were evaluated on growth characteristics of tomato seedlings (Solanum lycopersicum Mill) under salinity stress. The plants were grown 3 weeks in sand that were fed with Hoagland nutrient solution with or without 100 mM NaCl.ResultsThe results showed that salinity significantly reduced tomato seedling growth and traits of plant height, leaf area, shoot fresh weight, and nutrient concentration of potassium, calcium, iron and zinc compared to control plants. However, leaf SPAD value, root fresh and dry weights, leaf concentration of sodium, proline and soluble sugars were significantly increased under 100 mM NaCl salinity compared to control plants. Application of salicylic acid particularly by foliar pretreatment increased the tomato plant growth and those traits that were reduced by NaCl salinity. Application of SA, particularly foliar pretreatment, also increased the root fresh and dry weights, leaf proline and soluble sugars concentrations as compared with salinity alone. Foliar SA pretreatment significantly increased leaf K and Fe concentrations, whereas leaf Ca was significantly increased by either root or leaf pretreatment with SA under salinity.ConclusionThe results indicate that the most to least effective method of SA application was leaf pretreatment, root pretreatment and leaf treatment, respectively, to recover the reduced growth parameters of tomato plant under salinity stress.

Nowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords \"COVID19 virus\", \"2019 novel coronavirus\", \"Wuhan coronavirus\", \"2019-nCoV\", \"X-Ray Computed Tomography\", \"Polymerase Chain Reaction\", \"Reverse Transcriptase PCR\", and \"PCR Reverse Transcriptase\". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85–90%), 46% (95% CI 29–63%), 69% (95% CI 56–72%), and 89% (95% CI 82–96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

In the present research, a continuum-based thermo-mechanically coupled rigid-viscoplastic finite element model is proposed in Deform-3D software to simulate the friction stir welding (FSW) of AZ91 magnesium alloy. Then, to consider the microstructure evolution of the weld zone, a model is established based on the combination of cellular automaton and Laasraoui-Jonas models. The simulated microstructure of the weld zone is compared with the experiment one. Furthermore, the ability of presented model in demonstrating the nucleation and grain growth stages during dynamic recrystallization (DRX) is considered.

The paramount concern of highly accurate energy-efficient computing in machines with significant cognitive capabilities aims to enhance the accuracy and efficiency of bio-inspired Spiking Neural Networks (SNNs). This paper addresses this main objective by introducing a novel spatial power spike-timing-dependent plasticity (Spatial-Pow-STDP) learning rule as a digital block with high accuracy in a bio-inspired SNN model. Motivated by the demand for precise and accelerated computation that reduces high-cost resources in neural network applications, this paper presents a methodology based on COordinate Rotation DIgital Computer (CORDIC) definitions. The proposed designs of CORDIC algorithms for exponential (Exp CORDIC), natural logarithm (Ln CORDIC), and arbitrary power function (Pow CORDIC) are meticulously detailed and evaluated to ensure optimal acceleration and accuracy, which respectively show average errors near 10 –9 , 10 –6 , and 10 –5 with 4, 4, and 6 iterations. The engineered architectures for the Exp, Ln, and Pow CORDIC implementations are illustrated and assessed, showcasing the efficiency achieved through high frequency, leading to the introduction of a Spatial-Pow-STDP learning block design based on Pow CORDIC that facilitates efficient and accurate hardware computation with 6.93 × 10 –3 average error with 9 iterations. The proposed learning mechanism integrates this structure into a large-scale spatiotemporal SNN consisting of three layers with reduced hyper-parameters, enabling unsupervised training in an event-based paradigm using excitatory and inhibitory synapses. As a result, the application of the developed methodology and equations in the computational SNN model for image classification reveals superior accuracy and convergence speed compared to existing spiking networks by achieving up to 97.5%, 97.6%, 93.4%, and 93% accuracy, respectively, when trained on the MNIST, EMNIST digits, EMNIST letters, and CIFAR10 datasets with 6, 2, 2, and 6 training epochs.

Background The regulation of the circadian clock genes, which coordinate the activity of the immune system, is disturbed in inflammatory bowel disease (IBD). Emerging evidence suggests that butyrate, a short-chain fatty acid produced by the gut microbiota is involved in the regulation of inflammatory responses as well as circadian-clock genes. This study was conducted to investigate the effects of sodium-butyrate supplementation on the expression of circadian-clock genes, inflammation, sleep and life quality in active ulcerative colitis (UC) patients. Methods In the current randomized placebo-controlled trial, 36 active UC patients were randomly divided to receive sodium-butyrate (600 mg/kg) or placebo for 12-weeks. In this study the expression of circadian clock genes (CRY1, CRY2, PER1, PER2, BMAl1 and CLOCK) were assessed by real time polymerase chain reaction (qPCR) in whole blood. Gene expression changes were presented as fold changes in expression (2^-ΔΔCT) relative to the baseline. The faecal calprotectin and serum level of high-sensitivity C-reactive protein (hs-CRP) were assessed by enzyme-linked immunosorbent assay method (ELIZA). Moreover, the sleep quality and IBD quality of life (QoL) were assessed by Pittsburgh sleep quality index (PSQI) and inflammatory bowel disease questionnaire-9 (IBDQ-9) respectively before and after the intervention. Results The results showed that sodium-butyrate supplementation in comparison with placebo significantly decreased the level of calprotectin (-133.82 ± 155.62 vs. 51.58 ± 95.57, P -value < 0.001) and hs-CRP (-0.36 (-1.57, -0.05) vs. 0.48 (-0.09-4.77), P -value < 0.001) and upregulated the fold change expression of CRY1 (2.22 ± 1.59 vs. 0.63 ± 0.49, P -value < 0.001), CRY2 (2.15 ± 1.26 vs. 0.93 ± 0.80, P -value = 0.001), PER1 (1.86 ± 1.77 vs. 0.65 ± 0.48, P -value = 0.005), BMAL1 (1.85 ± 0.97 vs. 0.86 ± 0.63, P -value = 0.003). Also, sodium-butyrate caused an improvement in the sleep quality (PSQI score: -2.94 ± 3.50 vs. 1.16 ± 3.61, P -value < 0.001) and QoL (IBDQ-9: 17.00 ± 11.36 vs. -3.50 ± 6.87, P -value < 0.001). Conclusion Butyrate may be an effective adjunct treatment for active UC patients by reducing biomarkers of inflammation, upregulation of circadian-clock genes and improving sleep quality and QoL.

This study investigated the influence of various treatments on the autofluorescence properties of Bacillus subtilis . Our results demonstrate that autofluorescence is not a static property but is significantly modulated by environmental and nutritional factors. Glucose limitation markedly reduced autofluorescence, highlighting its crucial role in maintaining cellular components involved in fluorescence. Environmental stressors exerted diverse effects: oxidative stress induced by hydrogen peroxide altered fluorescence patterns, while ethanol exposure enhanced fluorescence intensity. Heat and cold stress induced subtle changes, suggesting a degree of bacterial resilience. Lysis methods, such as autoclaving and sonication, significantly impacted fluorescence intensity and spectral profiles, revealing insights into the cellular localization and nature of fluorophores. Microscopy analysis confirmed the presence of wavelength-specific autofluorescence in Bacillus subtilis . These findings underscore the sensitivity of bacterial autofluorescence to cellular and environmental perturbations and highlight its potential as a valuable tool for monitoring bacterial health and stress responses.

This work reports the synthesis of nickel ferrite decorated nitrogen and sulfur co-doped graphene quantum dot (NF@N, S:GQD) and its use as an electrode modifier. The developed NF@N, S:GQD modified glassy carbon electrode (NF@N, S:GQD/GCE) was applied to assess isoniazid (INZ) concentration based on its oxidation at the surface of the proposed electrode. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used as appropriate electrochemical techniques to study the electrochemical behavior of INZ and determine it. Based on combined evidence from surveys, research, and personal results, it is thought that the combination of nickel ferrite and doped graphene quantum dots can synergistically affect results, leading to increased sensitivity and reduced detection limits. This is probably mainly due to the high electrical conductivity of N, S-GQD structure, the electrocatalytic effect of nickel ferrite, and increased surface area resulting from the nano size of the modifier. The optimum conditions for preparing of the modified electrode and determination of INZ are selected by performing electrochemical experiments. The voltammetric response of the sensor is linear from 0.3 to 40 nM INZ under optimal conditions and the detection limit of the sensor is 0.1 nM. The validity and performance of the prepared sensor were confirmed by determining the amount of INZ in the drug and urine as real samples. The composite of doped nanoparticles and nickel ferrite is an innovative modification material to create electrochemical sensors with high sensitivity and selectivity that can be used in pharmaceutical applications.

Accurate segmentation of underwater pipelines is essential for marine infrastructure inspection. However, deep learning models often struggle with extreme underwater conditions such as low light, sea snow, and sea fog, leading to poor generalization on unseen data. Existing approaches typically focus on either accuracy or computational efficiency, leaving the challenge of achieving an optimal balance between the two unresolved. This paper introduces a novel hybrid architecture, the Swin Transformer-EFSNet fusion network, which delivers state-of-the-art accuracy with significantly reduced computational complexity and strong generalization capability. The model employs a dual-encoder design: a lightweight Swin Transformer branch to capture contextual relationships and a modified EFSNet branch optimized for efficient local feature extraction. Their outputs are dynamically integrated using a three-head cross-attention fusion module which prioritizes salient spatial and contextual information before decoding the final segmentation mask. We also present the HOMOMO dataset, a new benchmark containing images with challenging conditions such as low light, fog, sea snow, and complex occlusions (e.g., pipelines buried under sand or covered by vegetation). Extensive experiments on HOMOMO and two public datasets demonstrate that our method outperforms strong baselines, including UNet, SwinUNet, TransUNet, Mask2Former, YOLOv5, YOLOv11, and YOLOv12. On HOMOMO, our model achieves a mIoU of 98.44% and an F-boundary of 82.01%, surpassing the best-performing method by 8.43% and 5.34%, respectively. Crucially, the proposed model exhibits outstanding generalization to unseen data, demonstrating robustness against domain shifts. By effectively balancing global and local processing, our hybrid design achieves high accuracy without imposing heavy computational costs. These results establish a new paradigm for efficient and reliable visual perception in underwater environments, paving the way for practical autonomous inspection systems.

This paper investigates the effect of High-Performance Fiber-Reinforced Cementitious Composite (HPFRCC) with steel fibers on two fixed-end beams. Eight beams with identical sections were tested: two reference specimens and six reinforced with 1% or 2% steel fibers, with varied stirrup spacing near the beam ends. Results showed that fiber addition and stirrup adjustments markedly improved ductility and energy absorption. With 1% fibers, ductility nearly doubled, while 2% fibers more than doubled load capacity and tripled energy ductility. Adjusting loading points further enhanced strength and ductility. Overall, HPFRCC beams absorbed about 12% more energy than conventional concrete and extended the plastic hinge zone by nearly 50%.

BackgroundContamination of vegetable crops with heavy metals is a great threat to human health. On the other hand, monitoring plant tissue content of heavy metals at different growth stages could have important implications. In this study, shoot and root samples of garden cress and sweet basil were collected from five farms, from heavy metal polluted fields located in Shahre Rey, south of Tehran, Iran, in either young (3 weeks old) or mature (7 weeks old) plants. The concentrations of cadmium (Cd), lead (Pb), nickel (Ni), arsenic (As), chromium (Cr), cobalt (Co), copper (Cu), manganese (Mn) and zinc (Zn) in plant tissues were determined using atomic absorption spectroscopy. In another study, 2 weeks (young) or 6 weeks old (mature) plants of garden cress were subjected to three concentrations of Cd and Pb (0, 5, 10 mg L−1) under hydroponic sand culture for 5 days, in which Hoagland formula was used for nutrient solution preparation.ResultsThe results showed that root concentration of various heavy metals, particularly Cd, As, Ni, Co, Cu, Mn and Zn but not Pb were significantly higher than their shoot concentration in either crop under field sampling. The leaf concentration of some heavy metals was significantly different in seedling and older (mature) plant samples of either crop. Young plant leaves of sweet basil had significantly less Cd, Pb, As and higher Cu than mature plants, whereas young garden cress plants had similar Cd, Pb and higher As and Zn concentrations than mature plants. The Cr, Co, Mn and Zn concentrations were similar in young and mature plants of sweet basil. The Mn, Co, Cr and Ni concentration of young and mature plants of either crop was also similar. The result of hydroponic study showed that young plants of garden cress had higher potential to accumulate lead in shoot and root, particularly in lower (5 mg L−1) than higher (10 mg L−1) lead concentration; however, root Pb concentration at 10 mg L−1 Pb of nutrient solution showed no difference between young and mature plants. Regarding cadmium, young garden cress plants accumulated higher Cd than mature plants in their shoot, particularly under higher Cd levels (10 rather than 5 mg L−1) of nutrient solution; however, a wide difference in root Cd concentration was observed under low (5 mg L−1) than higher (10 mg L−1) cadmium concentration of nutrient solution.ConclusionThe results of these two studies indicate that despite that young plants have a higher potential for heavy metal uptake and accumulation, the low difference in young and mature plants in the polluted fields may be due to the longer period of plant growth of mature plants that may increase the risk of exposure to polluted air and dust deposition containing high levels of heavy metals.