Insights into bacterial community metatranscriptome and metabolome in river water influenced by palm oil mill effluent final discharge.

AIMS: This study aimed to investigate the effect of palm oil mill effluent (POME) final discharge on the active bacterial composition, gene expression, and metabolite profiles in the receiving rivers to establish a foundation for identifying potential biomarkers for monitoring POME pollution in rivers. METHODS AND RESULTS: The POME final discharge, upstream (unpolluted by POME), and downstream (effluent receiving point) parts of the rivers from two sites were physicochemically characterized. The taxonomic and gene profiles were then evaluated using de novo metatranscriptomics, while the metabolites were detected using qualitative metabolomics. A similar bacterial community structure in the POME final discharge samples from both sites was recorded, but their composition varied. Redundancy analysis showed that several families, particularly Comamonadaceae and Burkholderiaceae [Pr(>F) = 0.028], were positively correlated with biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). The results also showed significant enrichment of genes regulating various metabolisms in the POME-receiving rivers, with methane, carbon fixation pathway, and amino acids among the predominant metabolisms identified (FDR < 0.05, PostFC > 4, and PPDE > 0.95). This was further validated through qualitative metabolomics, whereby amino acids were detected as the predominant metabolites. CONCLUSIONS: The results suggest that genes regulating amino acid metabolism have significant potential for developing effective biomonitoring and bioremediation strategies in river water influenced by POME final discharge, fostering a sustainable palm oil industry.

Influence of Woven Glass-Fibre Prepreg Orientation on the Flexural Properties of a Sustainable Composite Honeycomb Sandwich Panel for Structural Applications.

Owing to the high potential application need in the aerospace and structural industry for honeycomb sandwich composite, the study on the flexural behaviour of sandwich composite structure has attracted attention in recent decades. The excellent bending behaviour of sandwich composite structures is based on their facesheet (FS) and core materials. This research studied the effect of woven glass-fibre prepreg orientation on the honeycomb sandwich panel. A three-point bending flexural test was done as per ASTM C393 standard by applying a 5 kN load on different orientation angles of woven glass-fibre prepreg honeycomb sandwich panel: α = 0°, 45° and 90°. The results show that most of the sandwich panel has almost the same failure mode during the three-point bending test. Additionally, the α = 0° orientation angle shows a higher maximum load prior to the first failure occurrence compared to others due to higher flexibility but lower stiffness. In addition, the woven glass-fibre prepreg orientation angle, α = 0°, has the maximum stress and flexural modulus, which directly depend upon the maximum load value obtained during the flexural test. In addition, the experimental results and analytical prediction for honeycomb sandwich deflection show good agreement. According to the result obtained, it is revealed that woven glass-fibre honeycomb sandwich panels with an α = 0° orientation is a good alternative compared to 45° and 90°, especially when better bending application is the main purpose. The final result of this research can be applied to enhance the properties of glass-fibre-reinforced polymer composite (GFRPC) cross-arm and enhance the existing cross-arm used in high transmission towers.

Humic acid recovery from stabilized leachate: Characterization and interference with chemical oxygen demand-colour removal.

Heterogeneous combinations of organic compounds (humic acid (HA) and fulvic acid) are the prime factor for the high concentration of colour and chemical oxygen demand (COD) in semi-aerobic stabilized landfill leachate. These organics are less biodegradable and cause a severe threat to environmental elements. Microfiltration and centrifugation processes were applied in this study to investigate the HA removal from stabilized leachate samples and its corresponding interference with COD and colour. The three-stage extraction process recovered a maximum of 1412 ± 2.5 mg/L (Pulau Burung landfill site (PBLS) leachate), 1510 ± 1.5 mg/L (Alor Pongsu landfill site (APLS leachate) at pH 1.5 and 1371 ± 2.5 mg/L (PBLS) and 1451 ± 1.5 mg/L (APLS) of HA (about 42% of the total COD concentration) at pH 2.5, which eventually indicates the process efficiency. Comparative characteristics analysis of recovered HA by scanning electron microscopy, energy-dispersive X-ray, X-ray photoelectron spectroscopy, and Fourier transform infrared significantly indicate the existence of identical elements in the recovered HA compared with the previous studies. The higher reduction (around 37%) in ultraviolet (UV) absorbance values (UV254 and UV280) in the final effluent indicates the elimination of aromaticity and conjugated double-bond compounds from leachate. Moreover, 36 and 39% COD and 39 and 44% colour removal exhibit substantial interference.

Calcium lignosulfonate modulates physiological and biochemical responses to enhance shoot multiplication in Vanilla planifolia Andrews.

Utilisation of calcium lignosulfonate (CaLS) in Vanilla planifolia has been reported to improve shoot multiplication. However, mechanisms responsible for such observation remain unknown. Here, we elucidated the underlying mechanisms of CaLS in promoting shoot multiplication of V. planifolia via comparative proteomics, biochemical assays, and nutrient analysis. The proteome profile of CaLS-treated plants showed enhancement of several important cellular metabolisms such as photosynthesis, protein synthesis, Krebs cycle, glycolysis, gluconeogenesis, and carbohydrate synthesis. Further biochemical analysis recorded that CaLS increased Rubisco activity, hexokinase activity, isocitrate dehydrogenase activity, total carbohydrate content, glutamate synthase activity and total protein content in plant shoot, suggesting the role of CaLS in enhancing shoot growth via upregulation of cellular metabolism. Subsequent nutrient analysis showed that CaLS treatment elevated the contents of several nutrient ions especially calcium and sodium ions. In addition, our study also revealed that CaLS successfully maintained the cellular homeostasis level through the regulation of signalling molecules such as reactive oxygen species and calcium ions. These results demonstrated that the CaLS treatment can enhance shoot multiplication in V. planifolia Andrews by stimulating nutrient uptake, inducing cell metabolism, and regulating cell homeostasis. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-023-01293-w.

Effect of natural antibacterial clays against single biofilm formation by Staphylococcus aureus and Salmonella Typhimurium bacteria on a stainless-steel surface.

Staphylococcus aureus and Salmonella Typhimurium have a propensity to develop biofilms on food contact surfaces, such as stainless-steel, that persist despite rigorous cleaning and sanitizing procedures. Since both bacterial species pose a significant public health risk within the food chain, improved anti-biofilm measures are needed. This study examined the potential of clays as antibacterial and anti-biofilm agents against these two pathogens on appropriate contact surfaces. Natural soil was processed to yield leachates and suspensions of both untreated and treated clays. Soil particle size, pH, cation-exchange capacity, and metal ions were characterized to assess their importance in bacterial killing. Initial antibacterial screening was performed on nine distinct types of natural Malaysian soil using a disk diffusion assay. Untreated leachate from Kuala Gula and Kuala Kangsar clays were found to inhibit S. aureus (7.75 ± 0.25 mm) and Salmonella Typhimurium (11.85 ± 1.63 mm), respectively. The treated Kuala Gula suspension (50.0 and 25.0 %) reduced S. aureus biofilms by 4.4 and 4.2 log at 24 and 6 h, respectively, while treated Kuala Kangsar suspension (12.5 %) by a 4.16 log reduction at 6 h. Although less effective, the treated Kuala Gula leachate (50.0 %) was effective in removing Salmonella Typhimurium biofilm with a decrease of >3 log in 24 h. In contrast to Kuala Kangsar clays, the treated Kuala Gula clays contained a much higher soluble metal content, especially Al (301.05 ± 0.45 ppm), Fe (691.83 ± 4.80 ppm) and Mg (88.44 ± 0.47 ppm). Elimination of S. aureus biofilms correlated with the presence of Fe, Cu, Pb, Ni, Mn and Zn irrespective of the pH of the leachate. Our findings demonstrate that a treated suspension is the most effective for eradication of S. aureus biofilms with a potential as a sanitizer-tolerant, natural antibacterial against biofilms for applications in the food industry.

Hydrogen gas sensing performance of a carbon-doped boron nitride nanoribbon at elevated temperatures.

In this study, computational simulations were used to investigate the performance of a carbon-doped boron nitride nanoribbon (BC2NNR) for hydrogen (H2) gas sensing at elevated temperatures. The adsorption energy and charge transfer were calculated when H2 was simultaneously attached to carbon, boron, and both boron and nitrogen atoms. The sensing ability was further analyzed considering the variations in current-voltage (I-V) characteristics. The simulation results indicated that the energy bandgap of H2 on carbon, boron, and both boron and nitrogen exhibited a marginal effect during temperature variations. However, significant differences were observed in terms of adsorption energy at a temperature of 500 K, wherein the adsorption energy was increased by 99.62% of that observed at 298 K. Additionally, the evaluation of charge transfer indicated that the strongest binding site was achieved at high adsorption energies with high charge transfers. Analysis of the I-V characteristics verified that the currents were considerably affected, particularly when a certain concentration of H2 molecules was added at the highest sensitivity of 15.02% with a bias voltage of 3 V. The sensitivity at 298 K was lower than those observed at 500 and 1000 K. The study findings can form the basis for further experimental investigations on BC2NNR as a hydrogen sensor.

Do customers' perceptions of Islamic banking services predict satisfaction and word of mouth? Evidence from Islamic banks in Bangladesh.

This study aims to investigate the customers' perceptions of Islamic banking services and their impact on satisfaction and word of mouth (WOM) with others. This study designs the bootstrapping procedures using a partial least square method to test path coefficient results. Structured questionnaires were distributed among clients of Islamic banks in Dhaka city, where 377 responses were collected for data analysis. The findings revealed that there is a highly significant relationship between security and customers' perception. Ethical responsibility and religious value have a positive and significant impact on customers' perception whereas benefit has a negative significant impact on customers' perception. Findings from this study also indicated that customers' perceptions mediate the effect of ethical responsibility, religious value, benefit, and security on satisfaction. In addition, customers' satisfaction mediates the effect of customers' perception and WOM. These findings can promote managers of Islamic banks to build customer satisfaction and WOM with Islamic banking services, and attain competitive advantage that may lead Islamic banks to succeed in the competitive business. This study also provides new insights into customers' WOM with others about Islamic banking services. This knowledge could assist Islamic banks to understand the customers' perceptions that would increase satisfaction and in turn, contribute to WOM with others in determining where would be best to target marketing attention of Islamic banking services with limited resources.

Diagnostic value of serum and tissue eosinophil in diagnosis of asthma among patients with chronic rhinosinusitis.

BACKGROUND: Chronic rhinosinusitis (CRS) is one of the most common chronic inflammatory diseases of sinonasal mucosa. Asthma among CRS patients is often underdiagnosed which makes the management of CRS more challenging. Therefore, using serum and tissue eosinophil as an indicator and predictor of asthma in CRS patients is vital for further preventing recurrent and increasing the effectiveness of treatment for CRS. OBJECTIVE: To determine the association and diagnostic ability of serum and tissue eosinophils in the diagnosis of asthma among CRS patients. METHODS: A cross-sectional study was conducted involving 24 CRS patients with asthma and without asthma, respectively, from the Otorhinolaryngology clinic of two tertiary hospitals located on the East Coast of Peninsular Malaysia. Serum and tissue eosinophils (obtained from nasal polyp) levels between both groups were compared. Association between serum and tissue eosinophils with asthma was evaluated using logistic regression analysis, adjusting for important sociodemographic characteristics. The diagnostic ability of serum and tissue eosinophil was then evaluated by assessing the receiver operating characteristic curve. RESULTS: A total of 48 CRS patients with a mean [SD] age of 47.50 [14.99] years were included. Patients with asthma had significantly higher serum [0.48 vs 0.35 × 109/L] and tissue eosinophil [100 vs 8.5 per HPF] levels. Tissue eosinophils were found to be an independent predictor of asthma with adjusted OR 1.05, p < 0.001, after adjusting for age and serum eosinophils. The area under the receiver operating characteristic curve for serum eosinophil was 69.0%. At optimal cut-off value (0.375 × 109/L), the sensitivity and specificity for serum eosinophil was 75.0% and 70.8%. The area under the receiver operating characteristic curve for tissue eosinophil was 93.4%. At the optimal cut-off value (58.0 per HPF), the sensitivity and specificity for tissue eosinophils were 79.2% and 91.7%, respectively. CONCLUSION: This study indicates a significantly higher level of serum and tissue eosinophils in CRS with asthma. However, there was no correlation between serum and tissue eosinophils in both group. Based on this study, the CRS patient needs to be screened for asthma if the level of serum eosinophil is > 0.375 × 109/L and tissue eosinophil > 58 per HPF.

Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation.

Goal: Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. Methods: A customized Convolutional Neural Network with mixup augmentation was trained with [Formula: see text]120 EEG trials for only one subject per model. Results: Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95% Confidence Interval: 0.908, 0.933) and 0.933 (95% Confidence Interval: 0.922, 0.945) classification accuracy, respectively. Conclusions: We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work.

Brief Virtual Reality Exposure Therapy and Its Effects on Negative and Positive Emotions Among Healthy Working Adults: A Feasibility Study.

Background: The primary purpose of this study is to examine the feasibility of a short-term virtual reality exposure therapy among healthy working adults sample. The secondary aim is to measure the effect of virtual reality exposure therapy on negative and positive emotions by comparing it to the standard stress management program and the wait-list groups. Methods: We enrolled 67 participants allocated into 3 groups to receive virtual reality exposure therapy, standard stress management, or wait-list group. The virtual reality exposure therapy group received a total of a 30-minute exposure to a virtual reality environment over 2 weeks. The standard stress management group received a stress management program once during the study period. Results: The results showed a heterogeneous sample, whereby a significantly younger, less-working years, and higher anxiety baseline score were found in the virtual reality exposure therapy group compared to standard stress management and wait-list groups. Nonetheless, the virtual reality exposure therapy group showed a reduction in depression, anxiety, and stress score (P < .001). The standard stress management group showed a reduction in anxiety score only (P = .002), whereas no significant changes were observed in the wait-list group. For positive emotion, all 3 groups showed significant improvement. Conclusion: Short-term virtual reality exposure therapy is a feasible intervention for the negative and positive emotions; however, cautious interpretation is needed due to significant heterogeneous sample. Replication of study with comparable groups is recommended.

Evaluation of Physico-Mechanical Properties on Oil Extracted Ground Coffee Waste Reinforced Polyethylene Composite.

The current work discusses ground coffee waste (GCW) reinforced high-density polyethylene (HDPE) composite. GCW underwent two types of treatment (oil extraction, and oil extraction followed by mercerization). The composites were prepared using stacking HDPE film and GCW, followed by hot compression molding with different GCW particle loadings (5%, 10%, 15% and 20%). Particle loadings of 5% and 10% of the treated GCW composites exhibited the optimum level for this particular type of composite, whereby their mechanical and thermal properties were improved compared to untreated GCW composite (UGC). SEM fracture analysis showed better adhesion between HDPE and treated GCW. The FTIR conducted proved the removal of unwanted impurities and reduction in water absorption after the treatment. Specific tensile modulus improved for OGC at 5 vol% particle loading. The highest impact energy absorbed was obtained by OGC with a 16% increment. This lightweight and environmentally friendly composite has potential in high-end packaging, internal automotive parts, lightweight furniture, and other composite engineering applications.

Ultrasonic Extraction of 2-Acetyl-1-Pyrroline (2AP) from Pandanus amaryllifolius Roxb. Using Ethanol as Solvent.

2-acetyl-1-pyrroline (2AP) is the compound that gives out the typical aroma and flavour of pandan leaves (Pandanus amaryllifolius Roxb.). This research incorporates ultrasonic extraction to extract the aromatic compound in pandan leaves. The parameters varied in this study are the extraction time, sonicator amplitude, concentration of solvent and the mass of pandan leaves. The experiment was conducted using a central composite design (CCD) model generated by the response surface methodology (RSM). From the extraction process, it can be deduced that the effect of leaves' mass is comparably higher than other parameters, while sonicator amplitude gives the most negligible impact on the process. The obtained p-value was 0.0014, which was less than 0.05. The high R-squared 0.9603 and adjusted R-squared 0.8809 indicate the model is well agreed with the actual data. The optimal control variables of ultrasonic extraction of 2AP are at an extraction time of 20 min, 60% of solvent concentration, amplitude of 25% and 12.5 g of pandan leaves, which produced 60.51% of yield of the extract and 1.43 ppm of 2AP. It is found that the mass of pandan leaves and the concentration of solvent have a significant impact on the extraction process of 2AP.

Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture.

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.

Advancement of Metatranscriptomics towards Productive Agriculture and Sustainable Environment: A Review.

While chemical fertilisers and pesticides indeed enhance agricultural productivity, their excessive usage has been detrimental to environmental health. In addressing this matter, the use of environmental microbiomes has been greatly favoured as a 'greener' alternative to these inorganic chemicals' application. Challenged by a significant proportion of unidentified microbiomes with unknown ecological functions, advanced high throughput metatranscriptomics is prudent to overcome the technological limitations in unfolding the previously undiscovered functional profiles of the beneficial microbiomes. Under this context, this review begins by summarising (1) the evolution of next-generation sequencing and metatranscriptomics in leveraging the microbiome transcriptome profiles through whole gene expression profiling. Next, the current environmental metatranscriptomics studies are reviewed, with the discussion centred on (2) the emerging application of the beneficial microbiomes in developing fertile soils and (3) the development of disease-suppressive soils as greener alternatives against biotic stress. As sustainable agriculture focuses not only on crop productivity but also long-term environmental sustainability, the second half of the review highlights the metatranscriptomics' contribution in (4) revolutionising the pollution monitoring systems via specific bioindicators. Overall, growing knowledge on the complex microbiome functional profiles is imperative to unlock the unlimited potential of agricultural microbiome-based practices, which we believe hold the key to productive agriculture and sustainable environment.

Influence of Particle Size and Zeta Potential in Treating Highly Coloured Old Landfill Leachate by Tin Tetrachloride and Rubber Seed.

Old leachate normally has a low organic compound content, poor biodegradability and is hard to biologically treat. The efficacy of tetravalent metal salts as a coagulant and the application of a natural coagulant as a flocculant in landfill leachate treatment is still inconclusive. Hence, this study aimed to evaluate the potential application of tin tetrachloride (SnCl4) as the main coagulant and the rubber seed (Hevea brasiliensis) (RS) as the natural coagulant aid as the sole treatment in eradicating highly coloured and turbid stabilised landfill leachate present at one of the old local landfills in Malaysia. The standard jar test conducted revealed that SnCl4 was able to eliminate 99% and 97.3% of suspended solids (SS) and colour, respectively, at pH8, with 10,000 mg/L dosages, an average particle size of 2419 d·nm, and a zeta potential (ZP) of -0.4 mV. However, RS was found to be ineffective as the main coagulant and could only remove 46.7% of SS and 76.5% of colour at pH3 with 6000 mg/L dosages, and also exhibited smaller particles (933 d·nm) with ZP values of -6.3 mV. When used as a coagulant aid, the polymer bridging mechanism in RS helped in reducing the SnCl4 concentration from 10,000 mg/L to 8000 mg/L by maintaining the same performances. The presence of 1000 mg/L RS as a coagulant aid was able to remove 100% of SS and 97.6% of colour. The study concluded that RS has the potential to be used together with SnCl4 in treating concentrated leachate with SS and colour.

Natural-Fiber-Reinforced Chitosan, Chitosan Blends and Their Nanocomposites for Various Advanced Applications.

There has been much effort to provide eco-friendly and biodegradable materials for the next generation of composite products owing to global environmental concerns and increased awareness of renewable green resources. This review article uniquely highlights the use of green composites from natural fiber, particularly with regard to the development and characterization of chitosan, natural-fiber-reinforced chitosan biopolymer, chitosan blends, and chitosan nanocomposites. Natural fiber composites have a number of advantages such as durability, low cost, low weight, high specific strength, non-abrasiveness, equitably good mechanical properties, environmental friendliness, and biodegradability. Findings revealed that chitosan is a natural fiber that falls to the animal fiber category. As it has a biomaterial form, chitosan can be presented as hydrogels, sponges, film, and porous membrane. There are different processing methods in the preparation of chitosan composites such as solution and solvent casting, dipping and spray coating, freeze casting and drying, layer-by-layer preparation, and extrusion. It was also reported that the developed chitosan-based composites possess high thermal stability, as well as good chemical and physical properties. In these regards, chitosan-based "green" composites have wide applicability and potential in the industry of biomedicine, cosmetology, papermaking, wastewater treatment, agriculture, and pharmaceuticals.

Synthesis of natural starch from Elaeis guineensis trunk biomass applying bisulphite steeping method: Optimization by RSM.

A massive quantity of Elaeis guineensis (oil palm) trunk biomass, containing a significant amount of natural starch, is available in Malaysia as biowaste because of annual replantation. The efficient extraction of this starch (carbohydrate polymer) would be worthwhile concerning the environmental sustainability and economy through conversion to bioresources. This study investigated the effectiveness of the bisulfite steeping method for starch synthesis from oil palm trunk (OPT) biowaste. The central composite design (CCD) of Design-Expert software executed an experimental model design, data analysis, evaluated the impacts of process variables and their interaction through response surface methodology to optimize the bisulfite steeping method for starch synthesis. The developed quadratic models for four factors (strength of sodium bisulfite solution, steeping hour, mixing ratio with the bisulfite solution, and ultrapure water) and one response (%Yield) demonstrated that a significant starch yield (13.54%) is achievable employing 0.74% bisulfite solution, 5.6 steeping hours, for 1.6 and 0.6 mixing ratio with the bisulfite solution and ultrapure water respectively. Experimental outcomes were consistent with the predicted model, which eventually sustains the significance of this method. Malvern Zetasizer test revealed a bimodal granular distribution for starch, with 7.15 µm of hydrodynamic size. Starch morphology was determined by scanning electron microscopy. X-ray diffraction investigation exhibits an A-type model, specifying persistent characteristics, while FTIR confirms the presence of hydroxyl, carboxylic, and phenolic groups like other cereal starches.Implications: Malaysia is the 2nd largest palm oil exporter in the world. About 110 million tons of palm oil trunk (OPT) biomass is available annually during replanting activities. Modification of bio-wastes into a beneficial form (only 22% presently) like starch extraction would ensure potential reuse as a natural coagulant for wastewater and leachate treatment, food source, adhesives towards boosting the country's economy by sustainable waste management. The current study achieved better starch yield (13.54%) than previous, from the OPT biomass through the novel bisulfite steeping method. Therefore, this method will ascertain the effective implication of numerous economic activities.

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.

Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.