Reclamation alters evapotranspiration and its biophysical controls in a meadow grassland on the Mongolian Plateau.

Global grasslands were constantly being replaced and reclaimed for cropland, and such reclamations may profoundly affect ecological such as water cycles. However, the long-term effects of this conversion on evapotranspiration (ET) processes remain underexplored. To discern changes in ET from grassland to reclaimed cropland and among different crop rotations, a four-year study (2018-2021) was conducted using the eddy covariance system in a Hulunber grassland and a neighboring reclaimed cropland. The ET in reclaimed cropland (248 mm) was 49% higher than the grassland (166 mm) during the growing season (crop growth period), whereas the ET in the grassland (134 mm) exceeded that in the cropland (128 mm) by 6% in the non-growing season. The croplands experienced a 19% increase in precipitation, primarily due to artificial irrigation during the growing season. Meanwhile, the increase in ET in reclaimed cropland might also be influenced by changes in vegetation type and crop growth characteristics, as well as by rational tillage practices that increase the cover of vegetation and biomass. Notably, potato cultivation most closely matched the water balance of grasslands. In addition, irrigation directly increased soil water content (SWC), and that enhancing the sensitivity of ET to SWC. Overall, this study highlighted the importance of understanding ET variations due to grassland conversion to cropland and different crop rotations, emphasizing the role of irrigation and tillage practices.

Fractionation of Boswellia serrata oleogum resin essential oil by short-path molecular vacuum distillation: Unveiling potent biological activities and chemical composition.

Boswellia serrata produces oleo gum resin, a rich source of essential oil (EO). EOs, produced as secondary metabolites by medicinal plants, are employed for medicinal and therapeutic purposes. The present study aimed to investigate the yield, chemical composition, antioxidant (AO), antimicrobial, and hemolytic activity of B. serrata EO and its fractions and sub-fractions (SFs). The EO was extracted using the superheated steam extraction (SHSE) method at 140°C. Short-path molecular vacuum distillation was used to separate the EO into fractions and SFs. Gas chromatography-mass spectrometry analysis showed α-pinene, α-thujene, trans verbenol, and linalool as major components of EO. The AO potential was evaluated using a 2,2-diphenyl-1-picrylhydrazyl assay, % inhibition in a linoleic acid assay, H2O2 scavenging assay, and total AO content (TAOC) using a ferric reducing AO power assay. F2b SF exhibited the highest scavenging activity, with percentages of 95.77%, 96.20%, and 83.54%, respectively, whereas EO revealed the highest TAOC value of 115.94%. Antimicrobial activity was evaluated by disc diffusion, resazurin microtiter plate, and microdilution broth assays. F1c SF showed maximum antibacterial potential (high inhibition zone 17.65-38.28 mm and low minimum inhibitory concentration [MIC] 2.20-84.44 µg/mL). The EO showed the highest antifungal activity (high inhibition zone 12.58-25.81 mm and low MIC 35.18-225.17 µg/mL). Cytotoxicity was assessed by hemolytic assay, with the F1c SF showing the highest activity at 10.89%. It is concluded that SHSE is an effective technique for B. serrata EO extraction, and this EO can be utilized for various medicinal purposes.

Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.

Fabrication, Characterization, and Performance Evaluation of Thermally Stable [5,6]-Fused Bicyclic Energetic Materials.

In recent decades, there has been considerable interest in investigating advanced energetic materials characterized by high stability and favorable energetic properties. Nevertheless, reconciling the conflicting balance between high energy and the insensitivity of such materials through traditional approaches, which involve integrating fuel frameworks and oxidizing groups into an organic molecule, presents significant challenges. In this study, we employed a promising method to fabricate high-energy-density materials (HEDMs) through the intermolecular assembly of variously substituted purines with a high-energy oxidant. Purines are abundant in nature and are readily available. A series of advanced energetic materials with a good balance between energy and sensitivity were prepared by the simple and effective self-assembly of purines with high-energy oxidants. Notably, these compounds exhibit incredibly improved crystal densities (1.80-2.00 g·cm-3) and good detonation performance (D: 7072-8358 m·s-1; P: 19.82-34.56 GPa). In comparison to RDX, these self-assembled energetic materials exhibit reduced mechanical sensitivities and enhanced thermal stabilities. Compounds 1-5 demonstrate both high energy and low sensitivity, indicating that self-assembly represents a straightforward and effective approach for developing advanced energetic materials with a balanced combination of energy and safety. Moreover, this study offers an avenue for synthesizing energetic materials based on naturally occurring compounds assembled through intermolecular attractions, thereby achieving a balance between energy and sensitivity along with versatile functionality.

Potential of ZnO nanoparticles for multi-drug resistant Escherichia coli having CRISPR-Cas from poultry market in Lahore.

BACKGROUND AND OBJECTIVES: Apart from known factors such as irrational use of antibiotics and horizontal gene transfer, it is now reported that clustered regularly interspaced short palindromic repeats (CRISPR) are also associated with increased antimicrobial resistance. Hence, it is critical to explore alternatives to antibiotics to control economic losses. Therefore, the present study aimed to determine not only the association of CRISPR-Cas system with antibiotic resistance but also the potential of Zinc Oxide nanoparticles (ZnO-NPs) for avian pathogenic Escherichia coli (APEC) isolated from poultry market Lahore. MATERIALS AND METHODS: Samples (n = 100) were collected from live bird markets of Lahore, and isolates were confirmed as Escherichia coli (E. coli) using the Remel One fast kit, and APEC was identified using PCR. The antibiotic resistance pattern in APEC was determined using the minimum inhibitory concentration (MIC), followed by genotypic confirmation of antibiotic-resistant genes using the PCR. The CRISPR-Cas system was also identified in multidrug-resistant (MDR) isolates, and its association with antibiotics was determined using qRT-PCR. The potential of ZnO-NPs was evaluated for multidrug-resistant (MDR) isolates by MIC. RESULTS: All isolates of APEC were resistant to nalidixic acid, whereas 95% were resistant to chloramphenicol and 89% were resistant to streptomycin. Nineteen MDR APEC were found in the present study and the CRISPR-Cas system was detected in all of these MDR isolates. In addition, an increased expression of CRISPR-related genes was observed in the standard strain and MDR isolates of APEC. ZnO-NPs inhibited the growth of resistant isolates. CONCLUSIONS: The findings showed the presence of the CRISPR-Cas system in MDR strains of APEC, along with the potential of ZnO-NPs for a possible solution to proceed. This highlights the importance of regulating antimicrobial resistance in poultry to reduce potential health consequences.

Matrix Viscoelasticity Tunes the Mechanobiological Behavior of Chondrocytes.

In articular cartilage, the pericellular matrix acting as a specialized mechanical microenvironment modulates environmental signals to chondrocytes through mechanotransduction. Matrix viscoelastic alterations during cartilage development and osteoarthritis (OA) degeneration play an important role in regulating chondrocyte fate and cartilage matrix homeostasis. In recent years, scientists are gradually realizing the importance of matrix viscoelasticity in regulating chondrocyte function and phenotype. Notably, this is an emerging field, and this review summarizes the existing literatures to the best of our knowledge. This review provides an overview of the viscoelastic properties of hydrogels and the role of matrix viscoelasticity in directing chondrocyte behavior. In this review, we elaborated the mechanotransuction mechanisms by which cells sense and respond to the viscoelastic environment and also discussed the underlying signaling pathways. Moreover, emerging insights into the role of matrix viscoelasticity in regulating chondrocyte function and cartilage formation shed light into designing cell-instructive biomaterial. We also describe the potential use of viscoelastic biomaterials in cartilage tissue engineering and regenerative medicine. Future perspectives on mechanobiological comprehension of the viscoelastic behaviors involved in tissue homeostasis, cellular responses, and biomaterial design are highlighted. Finally, this review also highlights recent strategies utilizing viscoelastic hydrogels for designing cartilage-on-a-chip.

Chemistry, biosynthesis, and theranostics of antioxidant flavonoids and polyphenolics of genus Rhododendron: an overview.

The genus Rhododendron is an ancient and most widely distributed genus of the family Ericaceae consisting of evergreen plant species that have been utilized as traditional medicine since a very long time for the treatment of various ailments including pain, asthma, inflammation, cold, and acute bronchitis. The chemistry of polyphenolics isolated from a number of species of the genus Rhododendron has been investigated. During the currently designed study, an in-depth study on the phytochemistry, natural distribution, biosynthesis, and pharmacological properties including their potential capability as free radical scavengers has been conducted. This work provides structural characteristics of phenolic compounds isolated from the species of Rhododendron with remarkable antioxidant potential. In addition, biosynthesis and theranostic study have also been encompassed with the aims to furnish a wide platform of valuable information for designing of new drug entities. The detailed information including names, structural features, origins, classification, biosynthetic pathways, theranostics, and pharmacological effects of about 171 phenolics and flavonoids isolated from the 36 plant species of the genus Rhododendron with the antioxidant potential has been covered in this manuscript. This study demonstrated that species of Rhododendron genus have excellent antioxidant activities and great potential as a source for natural health products. This comprehensive review might serve as a foundation for more investigation into the Rhododendron genus.

Antiseizure Medication Use and Outcomes After Suspected or Confirmed Acute Symptomatic Seizures.

Importance: Antiseizure medications (ASMs) are frequently prescribed for acute symptomatic seizures and epileptiform abnormalities (EAs; eg, periodic or rhythmic patterns). There are limited data on factors associated with ASM use and their association with outcomes. Objectives: To determine factors associated with ASM use in patients with confirmed or suspected acute symptomatic seizures undergoing continuous electroencephalography, and to explore the association of ASMs with outcomes. Design, Setting, and Participants: This multicenter cohort study was performed between July 1 and September 30, 2021, at 5 US centers of the Post Acute Symptomatic Seizure Investigation and Outcomes Network. After screening 1717 patients, the study included 1172 hospitalized adults without epilepsy who underwent continuous electroencephalography after witnessed or suspected acute symptomatic seizures. Data analysis was performed from November 14, 2023, to February 2, 2024. Exposure: ASM treatment (inpatient ASM continuation ≥48 hours). Main Outcomes and Measures: Factors associated with (1) ASM treatment, (2) discharge ASM prescription, and (3) discharge and 3-month Glasgow Outcome Scale score of 4 or 5 were ascertained. Results: A total of 1172 patients (median [IQR] age, 64 [52-75] years; 528 [45%] female) were included. Among them, 285 (24%) had clinical acute symptomatic seizures, 107 (9%) had electrographic seizures, and 364 (31%) had EAs; 532 (45%) received ASM treatment. Among 922 patients alive at discharge, 288 (31%) were prescribed ASMs. The respective frequencies of inpatient ASM treatment and discharge prescription were 82% (233 of 285) and 69% (169 of 246) for patients with clinical acute symptomatic seizures, 96% (103 of 107) and 95% (61 of 64) for electrographic seizures, and 64% (233 of 364) and 48% (128 of 267) for EAs. On multivariable analysis, acute and progressive brain injuries were independently associated with increased odds of inpatient ASM treatment (odds ratio [OR], 3.86 [95% CI, 2.06-7.32] and 8.37 [95% CI, 3.48-20.80], respectively) and discharge prescription (OR, 2.26 [95% CI, 1.04-4.98] and 10.10 [95% CI, 3.94-27.00], respectively). Admission to the neurology or neurosurgery service (OR, 2.56 [95% CI, 1.08-6.18]) or to the neurological intensive care unit (OR, 7.98 [95% CI, 3.49-19.00]) was associated with increased odds of treatment. Acute symptomatic seizures and EAs were significantly associated with increased odds of ASM treatment (OR, 14.30 [95% CI, 8.52-24.90] and 2.30 [95% CI, 1.47-3.61], respectively) and discharge prescription (OR, 12.60 [95% CI, 7.37-22.00] and 1.72 [95% CI, 1.00-2.97], respectively). ASM treatment was not associated with outcomes at discharge (OR, 0.96 [95% CI, 0.61-1.52]) or at 3 months after initial presentation (OR, 1.26 [95% CI, 0.78-2.04]). Among 623 patients alive and with complete data at 3 months after discharge, 30 (5%) had postdischarge seizures, 187 (30%) were receiving ASMs, and 202 (32%) had all-cause readmissions. Conclusions and Relevance: This study suggests that etiology and electrographic findings are associated with ASM treatment for acute symptomatic seizures and EAs; ASM treatment was not associated with functional outcomes. Comparative effectiveness studies are indicated to identify which patients may benefit from ASMs and to determine the optimal treatment duration.

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

Unpacking the environmental quality through the effects of natural resources, renewable energy consumption, banking development and industrial value addition: An empirical evidence from BRICS countries.

This study explores the association between natural resources rent, industrial value addition, banking development, renewable energy consumption, total reserves and environmental quality in the dynamic context of BRICS nations from 1995 to 2019. BRICS economies are responsible for global greenhouse gas emissions and confront pressing environmental challenges, including biodiversity loss and pollution. For the dependent variable, the environmental quality, the study constructed a composite index using PCA for all environmental indicators where interdependencies among variables are prevalent. Besides this, the study incorporates two interaction terms to determine the indirect influence of natural resource rent and banking development on environmental quality through the mediating role of industrial value addition. By applying the CS-ARDL technique, the outcomes of the study reveal that natural resources rent, industrial value addition, and total reserves positively influence ENQ, indicating the adverse consequences of industrial sectors on environmental quality and continued environmental degradation due to resource-intensive industrial production, underscoring the urgency of sustainable resource management. In contrast, banking development and renewable energy consumption negatively influence ENQ, signifying the positive role of developed banking sectors in supporting eco-friendly projects and enhancing environmental quality. This study offers valuable insights for policy interventions to foster a more sustainable future.

A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers.

This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reliant on a single biomarker derived from CiPA O'Hara-Rudy (CiPAORdv1.0) ventricular cell model without the hERG dynamic as input to the individual machine learning model, have limitations in capturing the complexity inherent in the comprehensive range of factors influencing drug-induced TdP risk. This study aims to overcome these limitations by proposing a stacking ensemble machine learning approach by integrating multiple in silico biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. The ensemble machine learning model consisted of three artificial neural network (ANN) models as baseline model and support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models as meta-classifier. The highest AUC score of 1.00 (0.90-1.00) for high risk, 0.97 (0.84-1.00) for intermediate risk, and 1.00 (0.87-1.00) for low risk were obtained using seven biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. Furthering our investigation, we explored the model's robustness by incorporating interindividual variability into the generation of in silico biomarkers from a population of human ventricular cell models. This study also enabled an analysis of TdP risk classification under high clinical exposure and therapeutic scenarios for several drugs. Additionally, from a sensitivity analysis, we revealed four important ion channels, namely, CaL, NaL, Na, and Kr channels that affect significantly the important biomarkers for TdP risk prediction.

Advances in gold catalyzed synthesis of quinoid heteroaryls.

This review explores recent advancements in synthesizing quinoid heteroaryls, namely quinazoline and quinoline, vital in chemistry due to their prevalence in natural products and pharmaceuticals. It emphasizes the rapid, highly efficient, and economically viable synthesis achieved through gold-catalyzed cascade protocols. By investigating methodologies and reaction pathways, the review underscores exceptional yields attainable in the synthesis of quinoid heteroaryls. It offers valuable insights into accessing these complex structures through efficient synthetic routes. Various strategies, including cyclization, heteroarylation, cycloisomerization, cyclo-condensation, intermolecular and intramolecular cascade reactions, are covered, highlighting the versatility of gold-catalyzed approaches. The comprehensive compilation of different synthetic approaches and elucidation of reaction mechanisms contribute to a deeper understanding of the field. This review paves the way for future advancements in synthesizing quinoid heteroaryls and their applications in drug discovery and materials science.

Association of LPP and ZMIZ1 Gene Polymorphism with Celiac Disease in Subjects from Punjab, Pakistan.

Celiac disease (CD) is a complicated autoimmune disease that is caused by gluten sensitivity. It was commonly believed that CD only affected white Europeans, but recent findings show that it is also prevailing in some other racial groups, like South Asians, Caucasians, Africans, and Arabs. Genetics plays a profound role in increasing the risk of developing CD. Genetic Variations in non-HLA genes such as LPP, ZMIZ1, CCR3, and many more influence the risk of CD in various populations. This study aimed to explore the association between LPP rs1464510 and ZMIZ1 rs1250552 and CD in the Punjabi Pakistani population. For this, a total of 70 human subjects were selected and divided into healthy controls and patients. Genotyping was performed using an in-house-developed tetra-amplification refractory mutation system polymerase chain reaction. Statistical analysis revealed a significant association between LPP rs1464510 (χ2 = 4.421, p = 0.035) and ZMIZ1 rs1250552 (χ2 = 3.867, p = 0.049) and CD. Multinomial regression analysis showed that LPP rs1464510 A allele reduces the risk of CD by ~52% (OR 0.48, CI: 0.24-0.96, 0.037), while C allele-carrying subjects are at ~2.6 fold increased risk of CD (OR 3.65, CI: 1.25-10.63, 0.017). Similarly, the ZMIZ1 rs1250552 AG genotype significantly reduces the risk of CD by 73% (OR 0.26, CI: 0.077-0.867, p = 0.028). In summary, Genetic Variations in the LPP and ZMIZ1 genes influence the risk of CD in Punjabi Pakistani subjects. LPP rs1464510 A allele and ZMIZ1 AG genotype play a protective role and reduce the risk of CD.

Comparative study on efficiency of surface enhanced coal fly ash and raw coal fly ash for the removal of hazardous dyes in wastewater: optimization through response surface methodology.

Crystal violet (CV) dye, because of its non-biodegradability and harmful effects, poses a significant challenge for wastewater treatment. This study addresses the efficiency of easily accessible coal fly ash (CFA)-based adsorbents such as raw coal fly ash (RCFA) and surface enhanced coal fly ash (SECFA), in removing CV dye from waste effluents. Various analytical techniques such as FTIR, XRD, SEM, TEM, BET, zeta sizer and zeta potential were employed for the characterization of the adsorbents and dye-loaded samples. BET revealed that RCFA possesses a surface area of 19.370 m2 g-1 and SECFA of 27.391 m2 g-1, exhibiting pore volumes of 0.1365 cm3 g-1 and 0.1919 cm3 g-1 respectively. Zeta-sizer and potential analysis showed the static charges of RCFA as -27.3 mV and SECFA as -28.2 mV, with average particle sizes of 346.6 and 315.3 nm, respectively. Langmuir and Freundlich adsorption isotherms were also employed for adsorption studies. Employing central composite design (CCD) of response surface methodology (RSM), the maximum CV removal was 81.52% for RCFA and 97.52% for SECFA, providing one minute contact time, 0.0125 g adsorbent dose and 10 ppm dye concentration. From the thermodynamic studies, all the negative values of ΔG° showed that all the adsorption processes of both adsorbents were spontaneous in nature.

Effects of ß-Mannanase Supplementation and Soyhull Inclusion on Production Performance, Economics, Egg Quality, Blood Biochemicals, Nutrient Digestibility, and Intestinal Morphology in Golden Brown Hens (RIR × Fayoumi) during Late Peak Production.

This study investigated the effects of the ß-mannanase enzyme and soyhulls on production performance, economics, egg quality, hematology and serum biochemistry, nutrient digestibility, gut morphology, digesta viscosity, and excreta consistency in laying hens during the late peak production phase (37 to 40 weeks of age). Golden brown hens (RIR × Fayoumi; n = 200) were fed a control diet (no soyhulls or enzymes) and diets containing four combinations, i.e., 3% soyhulls with 20 mg/kg ß-mannanase (D1), 3% soyhulls with 30 mg/kg ß-mannanase (D2), 9% soyhulls with 20 mg/kg ß-mannanase (D3), and 9% soyhulls with 30 mg/kg ß-mannanase (D4), for four weeks in four replicates of 10 birds each. Overall, a significantly higher (p < 0.05) feed intake, weight gain, feed conversion ratio, and water intake were calculated in the D2 group as compared to the control and remaining combinations of soyhulls and ß-mannanase. No mortality was recorded during the entire experiment. Economically, the D1 and D2 groups showed the best results as compared to the D3 and D4 groups. Egg quality parameters like egg weight, shell weight and shell thickness, yolk weight, albumen weight and height, and the Haugh unit remained unchanged (p > 0.05). Similarly, the D2 group showed significantly lower total cholesterol, LDL, and VLDL levels and enhanced gut morphology with greater villus width, height, crypt depth, and surface area across intestinal segments. Crude protein (CP), crude fiber (CF), crude fat, and ash digestibility were higher (p < 0.05) in the D1 and D2 groups compared to the control. Digesta viscosity, excreta consistency, and other egg quality parameters remained unaffected. In conclusion, the dietary inclusion of a combination of 3% soyhulls and 30 mg/kg ß-mannanase may have potential benefits for laying hens by improving some production performance and egg quality indicators and economics, lowering blood cholesterol, LDL, and VLDL levels, enhancing nutrient digestibility, and improving gut morphology without affecting egg quality.

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

Advances in organocatalyzed synthesis of organic compounds.

The recent advancements in utilizing organocatalysts for the synthesis of organic compounds have been described in this review by focusing on their simplicity, effectiveness, reproducibility, and high selectivity which lead to excellent product yields. The organocatalytic methods for various derivatives, such as indoles, pyrazolones, anthrone-functionalized benzylic amines, maleimide, polyester, phthalimides, dihydropyrimidin, heteroaryls, N-aryl benzimidazoles, stilbenoids, quinazolines, quinolines, and oxazolidinones have been specifically focused. The review provides more understanding by delving into potential reaction mechanisms. We anticipate that this collection of data and findings on successful synthesis of diverse compound derivatives will serve as valuable resources and stimulating current and future research efforts in organocatalysis and industrial chemistry.

Surface-enhanced Raman spectroscopy for the study of interaction of an antibacterial agent ([bis(1,3-dipentyl-1H-imidazol-2(3H)-ylidene)silver(i)]bromide) with Bacillus subtilis bacterial biofilms.

Bacterial resistance towards antibiotics is a significant challenge for public health, and surface-enhanced Raman spectroscopy (SERS) has great potential to be a promising technique to provide detailed information about the effect of antibiotics against biofilms. SERS is employed to check the antibacterial potential of a lab synthesized drug ([bis(1,3-dipentyl-1H-imidazol-2(3H)-ylidene)silver(i)] bromide) against Bacillus subtilis and to analyze various SERS spectral features of unexposed and exposed Bacillus strains by observing biochemical changes in DNA, protein, lipid and carbohydrate contents induced by the lab synthesized imidazole derivative. Further, PCA and PLS-DA are employed to differentiate the SERS features. PCA was employed to differentiate the biochemical contents of unexposed and exposed Bacillus strains in the form of clusters of their representative SERS spectra and is also helpful in the pairwise comparison of two spectral data sets. PLS-DA provides authentic information to discriminate different unexposed and exposed Bacillus strains with 91% specificity, 93% sensitivity and 97% accuracy. SERS can be employed to characterize the complex and heterogeneous system of biofilms and to check the changes in spectral features of Bacillus strains by exposure to the lab synthesized imidazole derivative.