Kuwait Journal of Science最新文献

Bioengineering has excellent potential in plant-based in vitro cultures for efficiently synthesizing desired plant products. Due to the rapid destruction of natural habitats, in vitro methods might save endangered species. Winged-Stem Canscora is an endangered medicinal herb used in traditional medicine for brain disorders. This study investigates the induction of in-vitro metabolite production using chitosan. Among the various concentrations of auxins tested, 82% of callus was obtained from Murashige and Skoog (MS) medium with 3 mg L⁻¹ Kinetin (KIN) + 1 mg L⁻¹ 1-Naphthaleneacetic acid (NAA). The highest callus content was after the 3rd subculture at 200 mg L⁻¹ chitosan treatment. On the 70th day of culture, levels of total phenolics and flavonoids are increased in the elicited and normal callus. However, antioxidant activity was higher in elicited callus compared to normal callus and wild plants. Higher secondary metabolite concentrations of elicited callus have superior anti-bacterial and anti-fungal activity. Secondary metabolites from elicited callus have a lower inhibitory concentration than those from wild plant and normal callus. High-performance thin layer chromatography (HPTLC) analysis showed that the elicited callus had higher amounts of mangiferin (0.178 μg mL⁻¹) and scopoletin (0.133 μg mL⁻¹). An efficient approach was employed to ensure that sufficient amounts of the widely used plant secondary metabolites from medicinal plants, such as mangiferin and scopoletin, were accessible to meet demand.

The worldwide emergence of multidrug-resistant bacterial infections among the population has impaired the existing effectiveness of antimicrobial treatments, necessitating the hunt for other alternatives. We aimed to find the antibacterial and antioxidant activities of Clematis montana and C. grata leaf extracts (both species have medicinal use among the local population). Extracts were assessed against four pathogenic bacterial strains (Escherichia coli, Salmonella typhi, Pseudomonas aeruginosa, and Staphylococcus aureus), through the agar well diffusion method. Total flavonoid content (TFC), total phenolic content (TPC), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity were quantified. Results suggested that all plant extracts, particularly those extracted with methanol, were potentially active in suppressing pathogenic bacterial growth (particularly against P. aeruginosa and E. coli) although their efficiency varied when compared to vancomycin. However, the antibacterial activity of methanolic extracts from both species was not greatly influenced by the method of extraction (maceration/soxhlet apparatus). TFC was significantly higher in the methanolic extracts of both species in comparison to the n-hexane extracts, whereas TPC and DPPH scavenging activity were detected to be highest in the methanolic extract from the leaves of C. grata. Our results suggested that the leaf extracts from both Clematis species, particularly the methanolic-based extract of C. montana, might serve as a promising source of an effective antibacterial agent.

The automation of classifying Arabic documents is becoming increasingly in demand, especially when dealing with an ever-growing amount of linguistic data. Natural language processing (NLP) has recently become one of the most significant fields in artificial intelligence (AI) thanks to recent advances in introducing transformer-based models. Transformers facilitate the use of reusable models by using pre-trained models (PTMs). This study aims to fine-tune monolingual (AraBERT (Antoun et al., 2020)), bilingual (GigaBERT (Lan et al., 2020)), and multilingual (XLM-RoBERTa (Conneau et al., 2020)) transformer-based encoder models to classify official Arabic correspondence in pre-defined classes and compare their predictive performance in terms of accuracy, using a new balanced dataset. The new balanced dataset has 22,741 Arabic texts and is categorized into six categories labeled with the most common ministries’ names. The results in this study show that GigaBERT achieved the highest accuracy rate of 98%. The implemented models may contribute to the domain of information systems (ISs) to facilitate the classification process in ministries without human intervention.

The study explores the complex relationship between quantitative and qualitative characteristics in strawberries grown using a vertical farming system and artificial full spectrum light (AFSL). The research reveals the interdependencies between traits, identifies direct, and indirect effects on yield and quality, and highlights critical factors influencing overall variation in strawberry characteristics using correlation analysis, path-coefficient analysis, and principal component analysis (PCA). These discoveries open the door for specialized breeding and cultivation techniques that optimize vertical farming procedures and increase the sustainability and productivity of strawberries. This study confirmed the suitability of a four-layered vertical farming system for strawberry cultivation; however, an additional supply of AFSL at lower levels of verticals ensures a higher yield. The number of fruits per plant and average berry weight had a high (p_ij > 0.3) degree of association with yield per plant at genotypic and phenotypic levels. The principal component analysis revealed a close association of T₁ (top level of vertical with natural light only), T₂ (third level from the top of vertical with natural light only), and T₅ (third level from the top of vertical with natural light and AFSL for 2 h) with yield contributing traits (number of bud formation, number of flowers, number of fruits per plant, fruit setting (%), fruit volume, average berry weight, average yield, and estimated yield per hectare).

The aim of this study was to investigate the spread of the genes algD and oprL in P. aeruginosa within the genetic structure of environmental sources of isolates and various clinical studies, and to evaluate a successful alternative for diagnosing Pseudomonas aeruginosa (P. aeruginosa) among various sources. In this study, 100 samples were collected from various clinical and environmental sources. Twenty-eight isolates of P. aeruginosa bacteria were isolated. Nine pathological samples were taken from wounds, burns, blood, and ear infections in three governorates: Salah al-Din, Baghdad, and Kirkuk. Five isolates of P. aeruginosa bacteria were collected from contaminated water from the Salah Al-Din and Baghdad governorates. The isolates were diagnosed phenotypically through the colony's shape, colour, odour and interaction with Gram stain. Additionally, the VITEK 2 system was used to confirm the diagnosis of the isolates. The results show that all the isolates belonged to the bacteria P. aeruginosa. The results of the molecular diagnosis using a polymerase chain reaction (PCR) technique for three genes with specialised primers (16SrRNA, algD, and oprL) show that most of the bacterial isolates from different sources belong to the type P. aeruginosa because they contain the specific gene 16SrRNA except for five isolates (M5, M7, M8, M9 and W1). Four of these belong to isolates with a pathogenic source and one was isolated from water. The results of the PCR for the algD gene show that all isolates contain the gene except for four diseased samples. Samples M5, M7, M8, and M9 were without the gene, while all isolates had the oprL gene. A sequencing analysis of the DNA was then performed for the isolates that were not associated with the specialised primers of the above genes to ensure their genera (M5, M7, M8, and M9), in addition to a sample of 15 isolated from soil (S6) that was associated with all special primers for the same genes for the purpose of comparison. The results show that isolates M5, M7, M8 and M9 were diseased isolates belonging to Escherichia coli, while the 15 isolated from soil (S6) were diagnosed as P. aeruginosa.

Fine particulate matter (PM_2.5) is a concern due to its health effects, necessitating critical monitoring for both quantity and variability. Utilizing low-cost sensors to track PM_2.5 is essential to augment other monitoring instruments, which are effective in generating temporal and spatial data. Therefore, in this study, we employed the low-cost PurpleAir (low-cost PA-II) to characterize the seasonal and diurnal variation of PM_2.5 in Bandung, Indonesia, representing the densely populated metropolitan area of Bandung. During the sampling period from June 2022 to May 2023, co-location sampling with the filter-based Super Speciation Air Sampling System (SuperSASS) was employed to assess the low-cost PA-II. The PM_2.5 data collected by the low-cost PA-II were compared to the SuperSASS data. The annual average mass concentration of PM_2.5 measured by SuperSASS and the low-cost sensor was 31.51 ± 15.53 μg/m³ and 39.04 ± 15.16 μg/m³, respectively, surpassing the Indonesian government's regulation limit for an annual average of 15 μg/m³. The comparative results of the two methods were obtained with R² = 0.96, and low-cost PA-II data was 1.24 higher than SuperSASS. The difference may be attributed to several factors, including differences in sensor technology, calibration, location, and data processing. The seasonal variation indicated an increase in concentration during the dry season and a decrease during the wet season. The diurnal pattern indicates the morning peak between 06:00 and 08:00 during the rush hour, as well as the evening peak between 18:00 and 23:00, attributed to low temperatures and stagnant conditions. The diurnal pattern of PM_2.5, which often exhibits the lowest peak at midday, is influenced by a combination of meteorological, atmospheric, and human activity factors. The findings suggested that the utilization of low-cost PA-II for a broader spatial scope is promising for real-time monitoring of PM_2.5, to increase air pollution awareness in society.

The aim of this study is to examine the thermoelectric properties alongside the structural, electronic, and magnetic properties of RbSrGe and RbSrSn d⁰-d⁰ half Heuslers. The full potential-linearized augment plane wave (FP-LAPW) system and Boltzmann transport equation are employed, utilizing classical Boltzmann approximation (CBA) in the background of density functional theory (DFT) as configured within WIEN2K. The structure optimization employed the PBE approximations and the Murnaghan equation of state, leading to a stable phase for RbSrGe and RbSrSn. The observed negative formation energy implies that the synthesis of RbSrGe and RbSrSn are possible. The half Heusler compounds under investigation were found to exhibit a narrow band gap in the majority spin direction and metallic character in the minority, which classifies these materials as half-metallic alloys based on the analysis of their electronic properties. The examination of elastic properties reveals that RbSrGe is brittle and RbSrSn is ductile. Critical analysis of thermoelectric properties demonstrates that RbSrGe and RbSrSn half Heuslers are possible materials for thermoelectrical applications.

Gold nanoparticles (AuNPs) are extensively employed in colorimetric sensing, taking advantage of their optical properties to detect variables via observable color changes. These properties are primarily driven by localized surface plasmon resonance (LSPR), particularly pronounced in AuNPs within the visible spectrum. In this study, AuNPs were synthesized via pulsed laser ablation in liquids (PLAL) with a laser pulse energy ($E_p$) ranging from 25 mJ to 75 mJ. Size distributions, hydrodynamic diameters, polydispersity indices (PDI), absorbance intensity, and LSPR were characterized. Spherical AuNPs with FCC structure were synthesized, exhibiting a maximum absorption peak centered at approximately 529 nm wavelength and a size range between 50 nm and 178 nm, easily adjustable depending on the laser pulse energy used in the synthesis process. An anomalous behavior was noted at $E_p=50$ mJ, exhibiting three peaks in size distribution, high PDI, low absorbance intensity, and indistinct LSPR. By extending the ablation time from 10 min to 30 min, particle size decreased alongside lower PDI. Size distributions transitioned from three to two peaks, absorbance increased, and LSPR became readily identifiable. These findings underscore the size control over AuNP characteristics achievable through PLAL synthesis parameters, promising significant implications for the optimization of colorimetric sensor design and development.

The ripening of climacteric fruit like guava is a complex process that is highly coordinated with its cellular backbone. In the present study, we combined microscopy, spectrophotometry, and statistical analysis to evaluate the anatomical changes in the pink variety of guava during five ripening stages (pre-ripe, ripe, color-turn, half over-ripe, and over-ripe) during its storage at room temperature (28±2 °C). The cholorophyll content of the peel, as determined by the measurement of chlorophyll a, b, and total chlorophyll, showed a significant decrease during the maturation process (4.05, 4.53, and 8.62 μg/cm², respectively, in the pre-ripe stage to ‘not detectable’ in the over-ripe stage). Gradual loss of integrity of the fruit pulp (pericarp) from the preserved bee-hive structure to cell mass was also monitored by studying the cellular anatomy with brightfield and scanning electron microscopy. The epidermal thickness and width of the cortical parenchyma cells revealed statistical differences from the initial pre-ripe stage to the final full-ripe stage. Finally, based on the cellular dimensions, multivariate analysis using PCA (Principal Component Analysis) tool grouped the stages into three clusters, namely, pre-ripe: ripe, color-turn: half-over ripe, and over-ripe stages. In conclusion, this study provided significant insights into cultivar-specific anatomical changes in guava fruit, with potential for future research to develop variants with longer post-harvest storage life.

In many studies, the Poisson regression model is mostly intended for modelling count responses. Recently, it was shown that the exploitation of the Poisson regression coefficient within the Koç (2021) ratio estimator increases the efficiency of the estimator. This study uses a new Poisson regression-based regression-type mean estimator with simple random sampling and finds its related mean square error formula. Essentially, we contrast the suggested estimators' mean square errors with those of previously published estimators. For the real data study, estimators were calculated for three real populations and the superior performance of the proposed estimator was observed. Similar results were obtained from the simulation study. As an outcome of these estimations, the proposed estimators are more effective than existing estimators. The empirical results verified the theoretical results to be remarkable.