Search

This research aims to isolate, characterize, and analyze pure compounds from Leea macrophylla leaf extract to investigate its antibacterial, antidiabetic, cytotoxic, and phytotoxic effects. Fresh leaves were collected, dried, and subjected to methanol extraction to obtain a crude extract. From the petroleum ether fraction (PEF) of this extract, three fractions—designated LM1, LM2, and LM3—were prepared using column chromatography. The fractions were tried to be characterized in search for single compound by instrumental technique like ATR-FTIR, 1H NMR and 13C NMR but the 1H NMR and 13C NMR spectra were found complex which were difficult to interpret. To dispel the doubt and get clear idea about the structure, GC-MS analysis of the compounds was carried out whose result showed that all the three extracts were decomposed to several small organic compounds that made the structure elucidation difficult. For this complication, the characterization of the extracts was not possible. Numerous compounds were identified in the methanol extract of L. macrophylla through GC-MS analysis. Among these compounds, Benzene, 1,2,3-trimethyl- and Undecane were found in higher percentages in LM1. LM2 contained Azulene and Bicyclo [4.4.1]undeca-1,3,5,7,9-pentaene, while LM3 was characterized by the presence of 9,9-Dimethoxybicyclo [3.3.1]nona-2,4-dione and 11-(2-Cyclopenten-1-yl)undecanoic acid, among others. The antibacterial activity of these fractions was evaluated against various bacterial strains, demonstrating broad-spectrum effectiveness. LM1 fraction showed the highest antibacterial activity against Proteus sp. With zone of inhibition 25 mm and weak activity against S. sonnei with zone of inhibition 5 mm. LM2 showed the highest activity to both E. cocci and P. aeruginosa with the zone of inhibition of 18 mm and comparatively lower but significant against Proteus sp. LM3 was highly active to S. sonnei with zone of inhibition 20 mm and lower but quite significant against Proteus sp. Moreover, the anti-diabetic potential was assessed, with LM1 showing the strongest α-amylase inhibitory activity, outperforming quercetin (standard). The IC50 values of LM1, LM2, LM3, and quercetin were 57.36 μg/mL, 100.66 μg/mL, 164.92 μg/mL, and 97.45 μg/mL, respectively. In addition, cytotoxicity was assessed using a brine shrimp lethality bioassay, and phytotoxicity was evaluated through seed germination and growth assays. The results suggest that L. macrophylla leaf extracts have potential applications in antimicrobial, antidiabetic, and anti-cancer contexts. This comprehensive study bridges gaps in knowledge surrounding L. macrophylla's multifaceted properties, offering insights into its therapeutic and ecological potential for healthcare and environmental management.

The study aimed to address the pervasive issue of inadequate nutrition in Bangladesh by analyzing eight distinct fish species, a pivotal step toward developing fish powder as a promising dietary supplement for nutritional enhancement. Samples from the Mathabhanga River in Chuadanga were rigorously processed and oven-dried to assess their nutritional properties, focusing on proximate composition, mineral content, and amino acid profile. Among the studied fish species, C. reba demonstrated the highest energy content (198.56 Kcal/100 g, p < 0.001), with notably elevated crude protein content (16.99 g/100 g, p < 0.001) compared to all except N. notopterus. The mineral composition order in fish powders was Ca > P > Fe > Na > Mg. Leucine emerged as the predominant essential amino acid (EAA) (6.84 to 32.15 mg/g, p < 0.001) among the species. O. pabo exhibited notably higher total EAA (∑EAA) levels (124.30 mg/g, p < 0.05) compared to other species. P. ticto showed a significantly higher ratio of total essential and non-essential amino acid content (∑EAA/∑NEAA) (0.41, p < 0.05) compared to other species. These findings underscored the nutritional value of the studied fish species, advocating their utilization as a dietary supplement to address malnutrition in Bangladesh.

Insects, particularly house fly pupae (M. domestica L.), are increasingly promoted as a rich and sustainable protein source, presenting a practical solution for organic waste management. These insects possess the capability to transform animal waste into valuable biomass, underscoring the necessity to evaluate housefly larval growth on diverse waste mixtures. The primary objective was to comprehend how different culture media influence the life history traits of this species. To address this, the research aimed to assess the growth and development performance of housefly larvae reared on various waste mixtures. Adult house flies (M. domestica) were sourced from meat and fish markets in Rajshahi city, Bangladesh, employing systematic sampling from different market sections for a representative selection. Experimental diets were formulated with combinations of cattle blood, cattle manure, rice bran, wheat bran, and kitchen waste, categorized into Diet-A (kitchen waste:cattle manure:wheat bran), Diet-B (cattle blood:rice bran), Diet-C (cattle manure:wheat bran), and Diet-D (cattle blood:kitchen waste:rice bran). Additionally, a control group comprised a mixture of waste materials, including yeast, egg white, milk powder, and wheat bran. The findings revealed that larvae, pupae, and adults reared on Diet-B, incorporating cattle blood and rice bran, demonstrated increased length compared to other diets. This varied diet also produced the heaviest larvae, pupae, and adults, exhibiting the highest morphometric parameters. The moisture content of pupae varied across the different diets, ranging from 53.23±0.17 % to 65.47±1.32 %. The pupae reared on Diet-B had the highest levels of crude protein (53.44±0.05 %, p < 0.05), while those reared on Diet-C had the highest levels of fat (18.56±0.05 %, p < 0.05 except for diet A). The findings demonstrate that housefly larvae can be reared on diets containing rice bran and cattle blood, which have different nutritional profiles and biological capacities. However, the ideal rearing strategy should depend on how M. domestica will be utilized in the future. The study highlights insects, especially housefly pupae, as a sustainable protein source and an organic waste management solution. Therefore, the results of this study contribute to the broader understanding of Musca domestica and offer practical implications for both scientific research and pest management practices.

Background: Chronic obstructive pulmonary disease (COPD) is the most prevalent long-term respiratory condition. Patients with COPD experience detrimental effects of COVID-19 infection. Objective: To figure out whether COPD is a risk factor influencing the progression of COVID-19 and to explore the clinical value of laboratory biomarkers to assess the severity of COVID-19 in patients with COPD comorbidity. Methods: In total, 1572 participants aged 35 to 70 years were enrolled to a tertiary hospital in Bangladesh between March 2022 and October 2022. Participants were categorized into four groups: (1) control, (2) COPD, (3) COVID-19, and (4) COVID-19 with COPD, and blood levels of clinical laboratory markers were assessed to analyze how these markers differ among the study groups. Results: COVID-19 patients with COPD had a significantly lower level of sodium (131.81 ± 2.8 mmol/L) and calcium (1.91 ± 0.28 mmol/L), and a significantly higher level of NT-proBNP (568.45 ± 207.40 pg/mL), bilirubin (1.34 ± 0.54 mg/dL), fibrinogen (577.27 ± 145.24 mg/dL), D-dimer (2.97 ± 2.25 μg/mL), C-reactive protein (71.08 ± 62.42 mg/L), interleukin-6 (166.47 ± 174.39 pg/mL), and procalcitonin (0.25 ± 0.30 ng/mL) compared to other study groups patients (p < 0.0001). In addition, the GOLD 4 group demonstrated significantly altered clinical parameters among COVID-19 patients with COPD. Furthermore, NT-proBNP, interleukin 6, D-dimer, C-reactive protein, and fibrinogen demonstrated excellent diagnostic performance in predicting disease severity among the COVID-19 patients with COPD, with a cut-off value of 511.2 pg/mL, 51.375 pg/mL, 1.645 μg/mL, 40.2 mg/L, and 510 mg/dL, respectively. Our results also indicate that inflammatory markers had significant positive correlations with the biochemical and coagulation markers in the COVID-19 patients suffering with COPD (p < 0.0001). Conclusions: NT-proBNP, interleukin 6, D-dimer, C-reactive protein, and fibrinogen are the most potential parameters for differentiating severe cases of COVID-19.

Among other motif finding algorithms Planted Motif Search (PMS) or (ℓ, d) motif search algorithms are considered as next-generation motif finding algorithms. Here, n strings and two integer ℓ and d are provided as input in PMS. It gives outputs of all sequences of length M in each input string, where each occurrence varies from Min at most d points. This paper proposes a new algorithm, for the (ℓ, d) motif discovery problem in which we find all strings of length ℓ that seems in every string of a provided set of strings with at most d mismatches has been. After proper investigation of the PMS problem, it has been shown that this is an NP-hard exact algorithm. Usually, in the worst-case scenarios, all the known exact algorithms for PMS need exponential time in some of the underlying parameters. In this paper, we proposed a faster approach that reduces the searching time in the sample-driven part of the algorithm. In particular, we used dynamic programming techniques to eliminate the recalculation of the values of some common subtree and introduced some new speedup techniques like using a linked list, reduced ℓ-mers for making the algorithm faster. Due to the use of Linked List as a main speedup technique, we named our proposed algorithm as Linked List Implementation technique of PMS8 (LL-PMS8).

In this study, the levels of indicator PCBs congeners and PAHs compounds were reported in cow milk from selected areas in Dhaka, Bangladesh, and the potential human health risks were assessed. A total of 100 milk samples were collected and analyzed using gas chromatography-tandem mass spectrometry (GC-MS/MS). Method validation was performed using recovery performance, linearity, limit of detection (LOD), and limit of quantification (LOQ) assays. PCBs congeners, including PCB No. 52 (2,2´,5,5´-tetrachlorobiphenyl), PCB No. 101 (2,2´,4,5,5´-pentachlorobiphenyl), PCB No. 153 (2,2,4,4,5,5–hexachlorobiphenyl), and PCB No 209 (Decachloro-1,1′-biphenyl perchlorobiphenyl) were detected, whereas PCB No 28 (2,2 ´,4–trichlorobiphenyl), PCB No. 138 (2,2´,3,4,4´,5´–hexachlorobiphenyl), and PCB No 180 (2,2´,3,4,4´,5,5´–heptachlorobiphenyl) were not detected in the analyzed milk samples. Among the 16 PAHs compounds, benzo (a) anthracene and chrysene were detected in milk samples. The Σ hazard risk index (HI) of all detected PCBs congeners was below the limit set by the European Food Safety Authority, which indicates limited health risks to animals and humans in the study area. However, the presence of PCBs and PAHs in milk samples from industrial areas may negatively affect human health, and further detailed studies are required to ensure public health safety.

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

Exploring and analyzing data using visualizations is at the heart of many decision-making tasks. Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that the weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for Visual Analysis (MIVA), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. The uniqueness of our approach is that unlike most previous approaches which typically support multimodal interactions with a single visualization, MIVA enables multimodal interactions with multiple coordinated visualizations of a dashboard that visually summarizes a dataset. We tested MIVA's applicability on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. We further empirically evaluated our system through a user study with twenty participants. The results of our study revealed that MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.

This paper presents a noble approach to develop a digital healthcare system for Bangladesh, a country which is soon going to become a developing country. We made an online application to help the masses to get access to healthcare easily and effectively. The application consists of two modules, doctor/hospital information system and emergency services. It has user-friendly features for doctor appointment making. Also, it has a blood donor search option, ambulance calling option, patient’s report view option, doctors ranking options etc. The doctors ranking is a special feature which was calculated based on doctor’s educational qualification, experience, average count of appointments made by patients and patient’s feedback. Feedback form real users showed that this information system was very helpful for them. If implemented countrywide, this system of digital healthcare may pave the way for an effective healthcare service in Bangladesh.

Background: Tour recommendation and path planning are the most challenging jobs for tourists as they decide Points of Interest (POI). Objective: To reduce the physical effort of the tourists and recommend them a personalized tour is the main objective of this paper. Most of the time people had to find the places he wants to visit in a difficult way. It kills a lot of time. Methods: To cope with this situation we have used different methodology. First, a greedy algorithm is used for filtering the POIs and BFS (Breadth First Search) algorithm will find POI in terms of user interest. The maximum number of visited POI within a limited time will be considered. Then, the Dijkstra algorithm finds the shortest path from the point of departure to the end of tours. Results: This work shows its users list of places according to the user's interest in a particular city. It also suggests them places to visit in a range from the location of the user where a user can dynamically change this range and it also suggests nearby places they may want to visit. Conclusion: This tour recommendation system provides its users with a better trip planning and thus makes their holidays enjoyable.

Now-a-days frequent mishaps like the road accident or other life-risking accident leading to operational emergencies are increasing day by day and the blood donor for such victims or patients are very hard to find in a short period. Sometimes it is really tough to collect the blood of the same group from the relatives of a patient in dying moments. The main objective of this work is to make a connection with these two groups who are in need of blood and who are willing to donate. We have determined the shortest distance between blood seeker and blood donors using the Dijkstra Algorithm. Anybody can contact the nearest blood donor of any group in a particular area quickly. By implementing this idea, the harassment of the victim can be lessened and so many lives can be saved.

Background and objective:: Skin cancer is recognized as the most common kind of cancer in the world. It could be deadly if not identified at the primary stage, which makes early detection very crucial. It is possible to identify it with the naked eye, but high inter-class similarity and intra-class variations make it too hard to detect. Due to the prevalence of this disease around the world, so far many automated systems have been developed based on deep learning to assist the physician in the early detection of skin lesions. Methods:: In this study, we propose a weighted average ensemble learning-based model to classify seven types of skin lesions. We used five deep neural network models, namely, ResNeXt, SeResNeXt, ResNet, Xception, and DenseNet as the base of the ensemble. For the training and evaluation of our models, we used 18,730 dermoscopy images collected from the official Human Against Machine (HAM10000) and ISIC 2019 dataset together with class balancing, noise removal, and data augmentation technique. We found the best combinations of the base models in the ensemble using the grid search method and optimized the impact of each base model for the average recall score. Results:: The five models performed excellent during evaluation and 88%, 89%, 91%, 88%, and 84% macro-average recall score were achieved by ResNeXt, SeResNeXt, DenseNet, Xception, and ResNet respectively. The simple average ensemble model boosted the result to 93% and the weighted average ensemble obtained a 94% recall score. The grid search method showed that the impact of all models are almost equal in the final model. Conclusion:: The average ensemble can improve the result by a significant amount. Our proposed system performed better than other existing systems and can support dermatologists for diagnosis.

Augmented reality (AR) has been widely used in education, particularly for child education. This paper presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing. The app has two main features: (i) guided learning to teach users how to write the alphabet and (ii) on-screen and AR-based handwriting testing using machine learning. A learner needs to write on the mobile screen in on-screen testing, whereas AR-based testing allows one to evaluate writing on paper or a board in a real world environment. We implement a novel approach to use machine learning for AR-based testing to detect an alphabet written on a board or paper. It detects the handwritten alphabet using our developed machine learning model. After that, a 3D model of that alphabet appears on the screen with its pronunciation/sound. The key benefit of our approach is that it allows the learner to use a handwritten alphabet. As we have used marker-less augmented reality, it does not require a static image as a marker. The app was built with ARCore SDK for Unity. We further evaluated and quantified the performance of our app on multiple devices.

The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm.

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Computational approaches could help in identifying deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in a disease related gene which is a difficult and laborious task through laboratory experiments. In the present study, we analyzed the impacts of nsSNPs on structure and function of Paraxonase 1 (PON1) using different bioinformatics tools. The human PON1 protein sequence and its corresponding gene's SNP information were collected from UniProt and dbSNP databases, respectively. We utilized SIFT, Polyphen, I-Mutant 2.0, MutPred, SNP and GO, PhD-SNP and PANTHER tools in order to examine the total 39 nsSNPs occurring in the PON1 coding region. We filtered the most pathological mutations by combining the scores of the aforementioned servers and found 8 SNPs (G344C, S302L, W281C, D279Y, H134R, F120S, L90P, C42R) as deleterious and disease causing. The PDB structure of PON1 protein was obtained from RCSB Protein Data Bank (PDB ID: 1V04). The deleterious SNPs in native PON1 were introduced using Swiss-PDB Viewer package and changes in free energy were observed for six out of eight mutant structures. Two SNPs, S302L (substitution of serine to leucine at 302 position in amino acid sequence) and L90P (substitution of leucine to proline at 90 position in amino acid sequence) caused the highest energy increase amongst all. The findings implicate that these nsSNPs would be analyzed further in detail to enumerate their possible association with the protein deteriorating and disease causal potentialities.