Colorectal cancer (CRC) is one of the most common malignancies worldwide, and assessment of microsatellite instability (MSI) provides important prognostic and therapeutic information. However, conventional MSI detection methods often rely on qualitative evaluation of fragment length alterations and may be challenging to implement in resource-limited settings. This pilot study evaluated the feasibility and preliminary amplification patterns of a quantitative PCR (qPCR)-based approach targeting the BAT-25 and BAT-26 microsatellite markers in formalin-fixed paraffin-embedded (FFPE) colon tissue samples. A cross-sectional descriptive study was conducted using fifteen FFPE tissue samples comprising ten colon cancer cases and five non-neoplastic colonic controls. Genomic DNA was extracted using a commercial FFPE DNA extraction kit, and qPCR was performed using SYBR Green chemistry to determine cycle threshold (Ct) values. Relative amplification differences were assessed using ΔCt values, with β-actin used as the reference gene. Both BAT-25 and BAT-26 demonstrated significant differences in ΔCt values between colon cancer samples and controls (p < 0.05). Exploratory logistic regression and receiver operating characteristic analyses indicated potential discriminatory ability, with area under the curve values of 0.72 for BAT-25 and 0.80 for BAT-26. BAT-26 showed higher specificity within this dataset. These findings suggest that qPCR-based amplification profiles of BAT-25 and BAT-26 may reflect molecular differences associated with MSI status between colon cancer and non-neoplastic colonic tissues. However, the small sample size and exploratory nature of this study require cautious interpretation, and further validation using larger cohorts and established MSI testing methods is necessary. This pilot study demonstrates the technical feasibility of a qPCR-based approach for preliminary assessment of MSI-associated patterns in FFPE samples and highlights its potential applicability in low-resource laboratory settings.
Achyranthes aspera, a medicinal plant traditionally used in Sri Lanka, exhibits antidermatophytic properties. This study aimed to formulate and evaluate topical herbal gels incorporating A. aspera leaf extract for activity against Trichophyton rubrum and Trichophyton mentagrophytes. Gels were formulated with different concentrations of A. aspera leaf extract: 1% (A), 3% (B), and 5% (C). Physicochemical properties, including pH, viscosity, and spreadability, and antifungal activity against clinical isolates of T. rubrum and T. mentagrophytes were evaluated using agar well diffusion, with ketoconazole as a positive control and 5% DMSO as a negative control. Stability was monitored over 15 days by assessing changes in physicochemical properties. All formulations were green, homogeneous, opaque, and smooth, with a semi-solid consistency; they maintained a skin-compatible pH (6.5–6.9) and stable physicochemical characteristics throughout the 15 days. All gels exhibited antifungal activity, with formulation C displaying the highest potency, producing zones of inhibition of 23.33 mm against T. rubrum and 24.33 mm against T. mentagrophytes. In comparison, the positive control yielded inhibition zones of 25.66 mm and 29.00 mm, respectively. In conclusion, the A. aspera leaf extract gel demonstrates moderate antifungal activity, particularly the 5% formulation, suggesting its potential as an alternative topical therapy for Tinea corporis. Further preclinical, clinical, and long-term stability studies are warranted.
Ensuring the dependable operation of modern software systems under dynamic and non-stationary operating conditions remains a major challenge in software reliability engineering. Although recent deep reinforcement learning (DRL)-based approaches have demonstrated promising capabilities for closed-loop adaptation of software reliability and testing effort, their black-box nature limits transparency, trustworthiness, and adoption in mission-critical and regulated domains. To address this limitation, we propose X-DRL-SRE, an explainable deep reinforcement learning framework for adaptive software reliability engineering. The framework integrates dynamic operational profile learning, level-wise reliability estimation, and a Proximal Policy Optimization (PPO)-based DRL agent with an explainability module that provides state-, action-, and outcome-level interpretations of reliability control decisions. Explainability is achieved through SHAP-based feature attribution for operational and code-level metrics, integrated gradients for policy sensitivity analysis, and counterfactual reasoning to justify testing effort reallocation under changing operational profiles. The proposed framework was evaluated using repeated stratified 10-fold cross-validation on NASA software defect benchmark datasets, including JM1, KC1, and PC1. Experimental results demonstrate that X-DRL-SRE improves reliability indices by 11.8–15.6% and reduces testing effort by 13.2–18.4% compared with LCSR-OPE, ML-ER-OPE, and non-explainable DRL baselines. Statistical significance was confirmed using paired t-tests and Wilcoxon signed-rank tests (p < 0.01), with medium-to-large effect sizes (Cohen’s d = 0.62–0.89). Furthermore, explanation stability scores exceeding 0.85 indicate that the generated explanations are reliable and consistent without compromising optimization performance. These findings demonstrate that explainability can be effectively integrated into adaptive reliability control, enabling transparent, interpretable, and high-performing software reliability engineering.
In recent years, growing global attention has focused on microplastic (MP) pollution and its environmental impacts. Various agronomic practices, such as plastic mulching, the use of plastic implements for transporting fertilizers and seedlings, and irrigation with MP-contaminated water, are potential sources of MP accumulation in agricultural soils. This study addresses the knowledge gap on plastic pollution in Sri Lankan agricultural soils by examining the occurrence, abundance, and vertical distribution of low-density MPs in selected vegetable fields in Nuwara Eliya. The sites, located in urban and semi-urban settings and under continuous cultivation for over a decade, provide valuable insights into long-term MP accumulation and transport within soil profiles. Thirty-six soil samples were collected from three depths (0–10, 10–30, and 30–50 cm) across two farmer-managed vegetable plots. Microplastics (density < 1.00 g/mL) were extracted through density separation following optimized chemical digestion in 100 mL of acidified water containing 5 mL of 30% H₂O₂ at 60–70 °C. MPs were categorized by shape and color under a stereomicroscope, and polymer types were confirmed by ATR-FTIR analysis. Statistical analysis using Minitab 18.0 compared MP concentrations between sites and depths. Results showed significant site-specific variation (p = 0.007), with the urban site (Site 2) exhibiting higher MP levels (340.0 ± 158.6 particles/kg) than the semi-urban site (246.7 ± 162.8 particles/kg), likely due to proximity to residential areas. MP abundance declined with depth, while particles smaller than 2 mm tended to migrate beyond the plough layer, accounting for 31% and 40% of total MPs at depths > 10 cm in Sites 1 and 2, respectively. Fragments dominated across all depths, though fibers penetrated deeper. The findings indicate widespread MP contamination in Nuwara Eliya’s vegetable soils, influenced by agricultural inputs and urban activities. Vertical migration of MPs may increase the risk of groundwater contamination, underscoring the need for monitoring and regulatory thresholds to manage microplastic pollution.
This study evaluated the effects of effective microorganism (EM) supplementation on feeding and drinking behavior, stress markers, physiological responses, udder health, and hematological parameters in lactating dairy cows. Ten Jersey crossbred cows (body weight = 403.78 ± 58.57 kg) of the same parity were randomly assigned to either a control group or an EM-supplemented group (10 mL kg-1 TMR) in a completely randomized design over a 12-week feeding trial. Behavioral observations and sample collections commenced eight weeks after trial initiation. Feeding and drinking behaviors were recorded using a CCTV camera and analyzed using BORIS software. Rectal temperature, heart rate, rumen motility, and udder health parameters were monitored weekly, while agonistic behaviors were recorded during the cows’ resting periods. Blood samples were collected from the jugular vein of each cow before trial initiation and on the final day of the feeding trial. Data were analyzed using the MIXED procedure in SAS software. Results revealed that EM-treated cows exhibited significantly (p<0.05) higher frequencies of feeding and drinking behaviors (4.51 ± 0.34 and 1.82 ± 0.31 events 2h-1, respectively) compared with the control group (2.31±0.34 and 1.02±0.31 events 2h-1, respectively). Allo-grooming (licking other cows) was significantly (p<0.05) higher in the EM-supplemented group ( 1.60 ± 0.42 events 2h-1 ) than in the control group (1.65 ± 0.42 events 2h-1 ), whereas other social interactions remained unaffected. Rumen movements were significantly (p<0.05) lower in EM-treated cows (3.31 ± 0.13 contractions min-1) compared with the control group (4.08 ± 0.13 contractions min-1), while rectal temperature and heart rate did not differ between treatments (p<0.05). The incidence of mastitis and stress markers did not differ significantly between treatment groups (p<0.05), and hematological parameters remained within standard reference ranges. In conclusion, dietary EM supplementation positively influences ingestive and social behaviors while maintaining stable physiological and health indices in lactating cows, highlighting its potential as a beneficial feed additive.
Global warming poses a significant threat to the planet, primarily driven by the excessive accumulation of carbon dioxide (CO₂) in the atmosphere. To mitigate this issue, various CO₂ adsorbents have been developed to capture and store the gas from the environment effectively. Among these, dendrimer-based adsorbents have emerged as promising materials due to their highly branched nanostructures, which enhance their adsorption and storage capabilities. Dendrimers consist of a well-defined inner core with multiple branching layers, and their unique structural variations influence their physicochemical properties. Topological indices provide mathematical representations of molecular structures and are instrumental in predicting the chemical and physical properties of compounds. In this study, we analyzed the topological indices of four different types of dendrimers: melamine-based dendrimers, Poly (amido amine) (PAMAM) dendrimers with a triethanolamine core, and P-dendrimers categorized into (1–Gx) and (2–Gx) generations. Specifically, we focus on two widely used topological indices, the Zagreb index and the Randić index, to evaluate these dendrimer structures. The computed indices offer valuable insights into the molecular characteristics of these dendrimers, aiding in the prediction of their adsorption efficiency and other physicochemical properties. The findings of this study contribute to the understanding of dendrimer-based CO₂ adsorbents and their potential applications in carbon capture technologies, paving the way for future advancements in environmentally sustainable solutions.
Medication counselling (MC) is an essential element of healthcare, particularly in pharmacy practice, as it promotes medication adherence and minimizes adverse effects. Hospital pharmacists play a crucial role by ensuring safe and effective use of medications. In Sri Lanka, the role of hospital pharmacists is largely restricted to dispensing medications, providing limited drug information, managing pharmaceutical inventories, and compounding extemporaneous preparations. This study aimed to evaluate perceptions, attitudes, and practices of pharmacists regarding MC at government hospitals in the Central Province of Sri Lanka. A cross-sectional study was conducted in October 2022 across 6 government hospitals in the Central Province of Sri Lanka. A validated, self-administered questionnaire was distributed among all eligible pharmacists (160) working at 6 selected government hospitals during the study period to gather demographic details, perceptions, attitudes, and practices on MC. The collected data were analysed via IBM SPSS software (version 25), and descriptive statistical methods were applied. Of the 160 pharmacists, 119 responded, and a majority (99.2%) affirmed that MC is a core responsibility, with 97.5% reporting their active involvement in this task. Notably, 92% engaged in counselling activities, and 68.9% had more than 10 years of experience. More than 80% routinely discussed essential details such as medication names, indications, administration routes, dosages, frequencies, treatment durations, and storage guidelines. The identified barriers to effective MC included time constraints (87%), limited knowledge (67%), lack of updated information (67%), and heavy patient loads (82%). Participants actively participated in MC and exhibited positive attitudes towards it. Many were seeking further knowledge to improve service quality. However, significant obstacles remain, such as time limitations, lack of updated knowledge, and high workload. The study recommends that policymakers, hospital administrators, and pharmacists collaborate on strategies to strengthen MC services, including the establishment of standard guidelines and patient education initiatives.
Corrosion remains a persistent global challenge, causing severe economic losses and material degradation across multiple industries, while the accumulation of human hair waste poses an emerging environmental burden due to its slow biodegradation and widespread disposal. Addressing these two pressing issues simultaneously, this study presents a sustainable strategy that transforms waste human hair into a value-added anti-corrosive coating material. Crude melanin was extracted from human hair via a base hydrolysis method and subsequently incorporated into a biodegradable poly(vinyl alcohol) (PVA) matrix with different melanin: PVA ratios to develop an eco-friendly coating. Successful extraction and characteristic functional groups of melanin were confirmed using Fourier Transform Infrared (FTIR) Spectroscopy. The corrosion protection performance of the developed coating was evaluated using Tafel polarization and Electrochemical Impedance Spectroscopy (EIS). The Tafel polarization revealed a substantial enhancement in corrosion resistance, achieving an inhibition efficiency of 89.4% for a 4000 ppm crude melanin coating compared to bare stainless steel. Furthermore, the study demonstrates the practical feasibility of the approach, where approximately 5.000 (±0.001) g of hair waste can produce sufficient crude melanin (12,000 ppm) to coat an 8 × 8 inch stainless steel surface with a thickness of 0.10 ± 0.01 mm. Unlike previous studies employing synthetic or microbial melanin, this work demonstrates the use of minimally processed crude melanin extracted directly from waste human hair as an environmentally sustainable anticorrosive coating material, providing a practical waste-to-value strategy for corrosion protection.
This work is motivated by transducers such as Hall-effect sensors and Integrated Electronics Piezoelectric (IEPE) devices, which share a common signal characteristic when measuring time-varying signals: a low-amplitude, time-varying component of interest superimposed upon a dominant, quasi-static direct current (DC) offset. While low-cost microcontrollers such as the ESP32 offer integrated analog-to-digital converter (ADC) capabilities, their utility for precision acquisition of such signals is limited by inherent nonlinearity, reference instability, and inadequate resolution. This paper presents an integrated hardware-firmware framework that strategically couples three complementary stages through a unifying dynamic range management strategy to systematically address these limitations. The analog front-end implements a phase-linear Sallen-Key Bessel filter to preserve signal morphology during anti-aliasing, followed by an instrumentation amplifier featuring programmable gain and an automated DC-offset nulling algorithm. The algorithm drives a 12-bit digital-to-analog converter (DAC) to adaptively center the signal within the ADC’s operating range via a binary search heuristic, without prior knowledge of the offset magnitude. To address the ESP32’s well-documented ADC nonlinearity, a piecewise linear calibration strategy was developed using a TL431 precision shunt reference and an internal DAC sweep across the full input range, generating a per-device, per-channel correction lookup table (LUT). The complete signal chain was characterized metrologically against a National Instruments data acquisition (NI DAQ) system used as an independent ground truth. Across three independent device units, the uncalibrated ADC exhibited errors of approximately 158.1±19.3 mV mean absolute error (MAE) (4.8 of full-scale). The self-contained piecewise linear calibration framework, requiring no external signal generator or factory calibration infrastructure beyond a single TL431 precision shunt reference, achieved an 88.8% improvement in measurement accuracy, reducing errors to 18.6±11.4 mV MAE (0.56% of full-scale), confirming that the calibration procedure is reproducible across hardware variants with per-device improvements ranging from 82.5% to 92.4%. These results demonstrate that the proposed framework effectively bridges the metrological gap between consumer-grade embedded silicon and laboratory-grade data acquisition (DAQ).
Microbial communities play a crucial role in the survival and fitness of mosquitoes, influencing multiple stages of their life cycle. This study aimed to characterize and compare the fungal communities associated with Aedes albopictus and Culex quinquefasciatus larvae and to evaluate the entomopathogenic potential of common fungal species isolated from both genera against Aedes albopictus and Culex quinquefasciatus. Larvae (n = 240) were collected from natural and artificial breeding sites in Kandy and Galle districts. Fungi from external surfaces and total larva extract were cultured on Potato Dextrose Agar (PDA) and identified morphologically and by DNA sequencing. Four fungal species common to both mosquito genera, Aspergillus fumigatus, A. niger, Cladosporium cladosporioides, and C. langeronii, were selected for entomopathogenicity assays. Bioassays were conducted using conidial suspensions (1 × 103–1 × 107 conidia/mL), and mortalities were recorded at 24 and 48 hours. Nine fungal species were isolated from Aedes larvae, with Curvularia sp. and Trichoderma sp. being the most abundant. Twenty fungal species were isolated from Culex larvae, with Cladosporium sp. predominating. Only A. niger exhibited strong larval pathogenicity against both mosquito species, with low LC₅₀ values (Ae. albopictus: 2.60 × 10⁶ and 2.17 × 10⁵ conidia/mL respectively at 24 h and 48 h; Cx. quinquefasciatus: 5.20 × 10⁵ and 4.05 × 10⁵ conidia/mL, respectively, at 24 h and at 48 h. Cladosporium cladosporioides showed the highest larvicidal activity against Cx. quinquefasciatus larvae (LC₅₀: 2.71 × 10⁵ conidia/mL; LC₉₀: 1.95 × 10⁶ conidia/mL). In contrast, Aspergillus fumigatus and C. langeronii showed relatively low larvicidal activity and were effective only against Cx. quenquifaciatus larvae. In adult bioassays, A. niger also showed notable pathogenicity against Ae. albopictus, with LC₅₀ of 7.98 × 10⁶ and 8.72 × 10⁵ conidia/mL at 24 and 48 hours, respectively. The results suggest that A. niger can be considered as an entomopathogenic fungus and could be recommended as a potential biological control agent against both larvae and adult mosquitoes.
The purpose of this study was to explore the use of AI-assisted tools in the higher education learning process by finding out the specific tools utilized by postgraduate students and exploring the associated benefits and challenges of their integration. A case study approach was adopted, focusing on postgraduate students studying Information Technology in Education at a state university. A sample of 100 students and 10 lecturers was selected using the purposive sampling method. Data were gathered through questionnaires, document analysis of 20 assignments, and semi-structured interviews with 20 students and 10 lecturers. Quantitative data were analyzed using descriptive methods, while qualitative data were analyzed using thematic analysis. The findings revealed that postgraduate students frequently used AI-assisted tools for learning, especially in the preparation of various classroom activities, including written assignments, coding tasks, and multimedia creation. Questionnaire data revealed that 100% of the students used AI-assisted tools in their classroom activities. The interview data suggested that benefits include language fluency, quick access to information, time-saving capabilities, coding assistance in various programming languages, and the ability to create multimedia content. However, lecturers identified several challenges, especially concerning academic integrity when evaluating classroom activities, assignments, and theses. They expressed concerns over maintaining academic standards, students’ critical thinking abilities, and ethical considerations when planning lessons and assignments. While AI-assisted tools can significantly enhance teaching and learning efficiency, their improper use may hinder students’ intellectual development. Therefore, when designing classroom activities and assessments, lecturers should focus on evaluating higher-order thinking skills. It is recommended that the university raise awareness among both students and lecturers regarding the ethical and responsible use of AI-assisted tools in teaching, and implement clear policies to regulate the use of AI-assisted tools in learning.
Rivers receiving untreated or poorly treated wastewater act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), posing serious environmental and public health risks. This study investigated bacterial loads, antibiotic-resistant Escherichia coli, and ARGs in the Mahaweli River in Kandy district, Sri Lanka, at two locations: a) at Gatambe, where the river received discharges from the canal Meda Ela (ME) and Kandy city wastewater treatment plant (WWTP), and b) at Haragama, where the river received the discharge of the Ntional Livestock Development Board (NLDB) farm. Water samples were collected from the river upstream, at discharge points, downstream, and from a buffalo pond inside the farm during two visits in 2024. Bacterial isolation, total coliform counts, and fecal coliform counts were conducted using standard microbiological methods. Susceptibility tests were conducted with E. coli isolates against six antibiotics, i.e., amoxicillin (AMX), tetracycline (TET), co-trimoxazole (COT), ciprofloxacin (CIP), ceftazidime (CAZ), and streptomycin (S), using the Kirby-Bauer disk diffusion method. Polymerase Chain Reaction (PCR) was used to detect ARGs blaCTX-M, tetA, aadA1, dfrA5/14, and qnrB. The highest microbial contamination and prevalence of antibiotic-resistant E. coli were recorded at the ME discharge point, with resistance highest to AMX at 87%, followed by CAZ at 43% and COT at 35%. At WWTP effluent, resistance to AMX and TET was 76% and 67%, respectively. Amoxicillin (50%) and TET (44%) resistance persisted downstream, while CIP resistance declined to 0%. Escherichia coli from the National Livestock Development Board (NLDB) farm discharge exhibited lower resistance, while water from the buffalo pond of the farm showed 50% resistance to AMX and TET, with the tetA gene. Multidrug resistance was 35% at ME and 48% at WWTP discharge points. All tested genes were detected in ME and WWTP discharges, except qnrB at WWTP. Strengthening wastewater treatment and effluent monitoring is essential to curb environmental dissemination and horizontal transfer of ARGs through Mahaweli water, which is an important source of drinking water.
Petroleum contamination is a prevalent environmental issue, with detrimental effects extending across diverse ecosystems. Among the numerous hydrocarbons present in petroleum, diesel is a major contributor to soil pollution. Previous studies have identified bacteria as highly effective agents for diesel degradation (DD), highlighting their potential for bioremediation of oil spills. However, successful bioremediation of diesel-contaminated environments requires the identification of effective diesel-degrading microbial strains, along with key traits that support DD, such as biosurfactant production. Therefore, this study aimed to identify efficient diesel-degrading bacteria (DDB) from petroleum-contaminated sites in Kandy District, while concurrently exploring their biosurfactant production capabilities. DDB were isolated using Bushnell-Haas broth supplemented with diesel. Pure bacterial isolates were obtained by streak and spread plating techniques. Isolated bacteria were identified through morphological and biochemical methods. The DD efficiencies of the isolates were examined by turbidity assay and 2,6-dicholophenolindophenol (DCPIP) assays. Moreover, efficient DDB were used to design artificial consortia. The degradation efficiencies of artificial consortia and natural consortia were also evaluated. Additionally, the biosurfactant production ability of efficient DDB was investigated by the emulsification test, oil displacement test, and CTAB agar plates. Among the diesel-degrading bacterial strains isolated, the four most effective isolates, in the order of decreasing efficiency, belonged to the genera Pseudomonas (83.04%), Staphylococcus (71.84%), Corynebacterium (62.61%), and Streptococcus (60.50%). Further, the DD efficiencies of most of the designed consortia and natural consortia were found to be significantly higher than those of the individual bacterial isolates, with one natural consortium containing Pseudomonas, Staphylococcus, and Corynebacterium that outperformed the designed consortia. Moreover, the isolated Pseudomonas and Staphylococcus strains were identified as biosurfactant producers, suggesting that their ability to produce biosurfactants contributes to their highest DD efficiencies. The findings of this study can be used in developing locally adaptable bioremediation strategies for diesel-contaminated sites in Sri Lanka.
Carrageenan is a commercially important polysaccharide extracted from seaweeds and widely used in the food, pharmaceutical, and cosmetic industries due to its gelling, thickening, and viscosity-enhancing properties. This study aimed to compare the quantity and quality of extracted refined carrageenan using four common extraction methods: conventional Ca(OH)2 autoclaving, 6% (w/v) Ca(OH)2, 6% (w/v) KOH, and 6% (w/v) NaOH. Forty-five days old brown color morphotype of Kappaphycus alvarezii grown in Jaffna was used as a seaweed source. Carrageenan yield, transmittance under Fourier Transform Infrared (FTIR), gel strength, water holding capacity, and L*a*b* color values were measured across extraction methods. Kappaphycus alvarezii cultivated in Jaffna yielded a carrageenan content exceeding 25%, with the highest recovery (37.20%) obtained using the KOH extraction method, which was significantly greater (P<0.05) than yields obtained with the other three extraction methods. However, the FTIR spectra for four extracted carrageenan samples displayed similar patterns with the commercial carrageenan sample, with slight deviations at 845 cm-1, 925-930 cm-1, and 1220-1260 cm-1 wave ranges indicating different types of kappa-, iota- and lambda-carrageenan fractions of different extraction methods. The highest (P<0.05) gel strength (1745.86 g/cm2) was recorded in the KOH method, whereas the conventional method recorded the significantly lowest gel strength (706.26 g/cm2). Moreover, conventional and KOH-treated carrageenan samples showed the highest (P<0.05) water holding capacity (98.59% and 97.42%) along with the highest a* and b* values. By contrast, lightness (L*) did not show a significant difference among carrageenan extracted from four methods. Therefore, it can be concluded that treatment with 6% KOH produces refined carrageenan with the highest yield and desirable physicochemical properties, outperforming the other methods when K. alvarezii is used as the seaweed source. Furthermore, this study highlights the potential for significant advancement in the use of 6% KOH method for domestic superior carrageenan production in Sri Lanka.
Acinetobacter baumannii, a multidrug-resistant Gram-negative bacterium, is increasingly recognized as an oral pathogen. With rising resistance to available antibiotics, plant-derived alternatives are gaining attention. This study aimed to evaluate the antibacterial activity of extracts of Mimusops elengi seed, Aloe barbadensis gel, and root extracts of Cocos nucifera, Areca catechu, and Piper nigrum against A. baumannii. Plant extracts were tested individually as well as in different combinations to evaluate the synergistic activity. Dental plaque samples were collected and cultured in nutrient media, and pure colonies of bacterial isolates were identified based on morphology. Based on the occurrence frequency, the isolated Morphotype A, was used for Anti-Bacterial Susceptibility Testing (ABST) via the disk diffusion method. The identity of the bacterium was confirmed by 16S rRNA gene sequencing. Crude plant extracts were prepared using methanol and subjected to ABST, individually and in combinations (1:1:1 ratio). Two, 0.2 % chlorhexidine containing mouthwashes (Positive) and sterile distilled water (Negative) were used as controls. According to ABST results, A. catechu (13.573 ± 0.0200 mm) and C. nucifera (13.108 ± 0.0366 mm) extracts showed moderate inhibition, while M. elengi showed slightly higher inhibition (14.500± 0.0643 mm). Aloe barbadensis showed the least zone of inhibition (9.017 ± 0.0328 mm), whereas P. nigrum had no effect. The combination of extracts (15.853 ± 0.0288 mm) and chlorhexidine mouthwashes (15.028 ± 0.0413 mm, 15.815 ± 0.0683 mm) also produced comparable inhibition zones, with no statistically significant difference (p > 0.05). The antibacterial activity of the combined extracts did not significantly differ from the individual effects of A. catechu or C. nucifera. Overall, M. elengi seed, A. catechu, C. nucifera root, and A. barbadensis gel extracts exhibited notable antibacterial activity against A. baumannii, individually as well as in combinations, highlighting their effectiveness as plant-based alternatives for combating A. baumannii infections.
The present study investigated the potential of two cyanobacterial species, namely Nostoc ellipsosporum (NE) and Spirulina subsalsa (SS), to remediate textile wastewater (TWW) while obtaining wastewater-grown biomass for biodiesel production. This study reports their multi-faceted benefits using unsterilized and undiluted (100%) TWW for the first time. Both cyanobacterial species were cultivated in TWW under greenhouse conditions, focusing on their growth, TWW decolorization, pollutant removal, phytotoxicity, lipid content, and fatty acid profile. Results demonstrated significant growth and decolorization of TWW by both species, highlighting their potential for sustainable treatment of TWW. NE-treated TWW (NE-TWW) achieved a chemical oxygen demand (COD) removal efficiency of 96.48%, while SS-treated TWW (SS-TWW) reached 93.11%. Ammonia removal efficiencies were recorded as 76.28% for NE-TWW and 96.86% for SS-TWW. NE-TWW and SS-TWW achieved nitrate removal of 69% and 73%, respectively. Phosphate removal was 81.14% for NE-TWW and 33.33% for SS-TWW. Both species exhibited more than 75% removal of heavy metals, particularly Cr, Fe, Zn, Cu, and Ni. Seed germination studies showed enhanced germination and seedling growth of green gram (Vigna radiata) when irrigated with NE-TWW and SS-TWW, indicating phytotoxicity reduction in treated TWW. Lipid yields (w/w) of 21.5% and 25% were recorded from TWW-grown biomass of NE and SS, respectively. Both species exhibited favorable fatty acid methyl ester (biodiesel) profiles, dominated by palmitic acid (C16:0), oleic acid (C18:1), stearic acid (C18:0), lauric acid (C12:0), and myristic acid (C14:0), indicating their suitability as biodiesel feedstocks. Overall, this integrated approach not only provides an eco-friendly and cost-effective strategy for TWW treatment but also offers a sustainable feedstock for biodiesel production.
High-valued black pepper-based products are adulterated with papaya (Carica papaya) seeds, and chilli (Capsicum annuum) seeds. Those are causes of food allergic reactions and food poisonings. Therefore, a cost-effective and efficient system should be developed to identify those adulterants. According to studies, each plant material has its own unique microscopic image fingerprint. Phase contrast microscopes are able to differentiate those features based on their refraction indexes. This research was developed for identifying the best deep learning architecture to differentiate adulterated black pepper powder from genuine black pepper powder by using microscopic images. TensorFlow deep learning models (InceptionV3, Inception ResNetV2, ConvNeXtLarge, Xception, VGG-19, and ResNet-50) were used as the backbone. The Adam optimizer and categorical cross-entropy as a loss function were used in both the evaluation stages and the training stages. Categorical Accuracy, Precision, F1Score, False Negatives, False Positives, True Negatives, and True Positives were used for evaluating the trained models. 0.001 and 0.0001 Fine-tuning with learning rates were used for model training. According to the results of evaluation matrices, InceptionV3, Xception, and ResNet-50 showed superior performances in differentiating genuine black pepper powder from other powder classes. But overfitting was identified when increasing the number of training epochs at lower learning rates. Therefore image segmentation should be introduced for overcoming the challenges in whole image labelling. Selective hierarchical neural network layer fine-tuning and hyper parameter tuning can be implemented for overcoming the model training based issues. According to the findings, those microscopic image based techniques are able to identify genuine black pepper powder from other adulterated black pepper powder.
Pyrite sulfur and fossil diatom analysis, coupled with the radiocarbon dating, provides important information regarding the sedimentary environment and past sea level fluctuations. Since this knowledge is beneficial in predicting future climate change patterns and sea level variations, the present study examines the paleo sea-level changes of Fujii, Okayama Prefecture, Japan, using fossil diatom assemblage and sedimentary pyrite formation. Based on the pyrite sulfur content, a marine environment has been developed at a depth of 1.8 meters around 6,745 +/- 80 years BP. This result has been verified with the finding of high pyrite sulfur percentage and the presence of marine diatoms. Based on the paleo salinity variations defined by concentrations of pyrite sulfur, a sea level rising period has been observed from 11,035 +/- 160 years BP to 6,745 +/- 80 years BP (from 2.4 m to 1.8 m depth), and it perfectly aligns with the sea level changes in the early Holocene period. Additionally, the diatom assemblage in this depth change from freshwater (i.e. Navicula sp., Melosira sp.) to marine (i.e. Melosira architecturalis Brun, Nitzschia sp.), supporting the findings. Finally, the reducing pyrite concentration above 1.8 m reflects the reduced marine influence due to receding sea levels and the introduction of more freshwater, which is supported by the presence of more freshwater diatoms.
