Morusacerane: A new gammacerane triterpenoid from the trunk of Morus Alba linn. with α-glucosidase inhibitory activity.

This phytochemistry investigation on the trunk of Morus alba L. resulted in the isolation of three triterpenoids, including a new gammacerane triterpenoid - morusacerane (1); along with two known compounds of betulinic acid (2) and ursolic acid (3). The structure elucidation was thoroughly conducted based on 1D, 2D-NMR and HRESIMS spectra, followed by a comparison with existing literatures. The evaluation on α-glucosidase inhibitory exhibited the great potential of the application of these isolated compounds in diabetes treatments. The results show that morusacerane (1), betulinic acid (2), and ursolic acid (3) demonstrate the strong inhibitory with the IC50 values of 106.1, 11.12, and 7.20 µM, respectively. All of these compounds interacted well with the allosteric site enzyme α-glucosidase MAL32 through H-bonds and hydrophobic interaction.

Fe3O4/Graphene Oxide/Chitosan Nanocomposite: A Smart Nanosorbent for Lead(II) Ion Removal from Contaminated Water.

A new graphene oxide (GO) nanocomposite that contains chitosan, a biological polymer, combined with a magnetic nanoparticle inorganic material (Fe3O4) was successfully prepared and applied for the adsorption of Pb(II) from aqueous solutions. The structural and morphological properties of the GO/Fe3O4/CS (GFC) nanocomposites were characterized by X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Influent factors for Pb(II) adsorption, including the contacting time, pH of the working medium, working temperature, and adsorbent dosage on the adsorption efficiency, have been optimized. Under optimized conditions, the adsorption isotherm results indicated that the Langmuir model provided a better description for the adsorption of Pb(II) onto the GFC nanosorbent than the Freundlich model. The maximum adsorption capacity (qmax) was 63.45 mg g-1. The pseudo-second-order kinetic model (R2 = 0.999) was fitted with the experimental results, implying that the adsorption of Pb(II) onto GFC is a chemical process. The thermodynamic studies demonstrated the exothermic nature of the adsorption process. Another advantage of the GFC nanosorbent for Pb(II) removal is its capability to be easily recovered under the use of an external magnet and subsequently regenerated. Our work demonstrated that the removal efficiency was stable after several regeneration cycles (i.e., approximately 12% reduction after four successive adsorption-desorption cycles), implying that the GFC nanosorbent exhibits satisfactory regeneration performance. Therefore, with high removal efficiency, high adsorption capacity, and stable reusability, the GFC nanocomposite is a remarkable application potential adsorbent for the in situ treatment of Pb(II) ion-containing aqueous solutions.

Tinctoric acid A-B, two new hopan-type triterpenoids from the Vietnamese lichen, Parmotrema tinctorum (Despr. ex Nyl.) hale with α-glucosidase inhibitory activity.

Two new hopan-type triterpenoids, namely tinctoric acid A-B (1-2), were isolated from the lichen Parmotrema tinctorum (Despr. ex Nyl.) Hale. Their structures were elucidated by extensive spectroscopic analyses (1D and 2D NMR). The absolute configuration at C-22 of 1 was established through DP4 probability. Compounds 1-2 were evaluated for their inhibitory activity against α-glucosidase and found to be more potent than those of positive control (acarbose, IC50 168 µM) with values IC50 74.7 and 98.2 µM, respectively. Both of these compounds interacted well with enzyme α-glucosidase MAL32 through H-bonds and hydrophobic interaction.

Annona glabra L. Seeds: An Agricultural Waste Biosorbent for the Eco-Friendly Removal of Methylene Blue.

The seeds of Annona glabra L., an invasive plant in Vietnam, were first employed as a new biosorbent for the adsorption of methylene blue (MB) from aqueous media. The characterizations of the material using FT-IR, SEM, nitrogen adsorption-desorption analysis, and point of zero charge reveals that it possesses a rough and irregular surface, various polar functional groups, and pHpzc of 5.5. Certain adsorption conditions including adsorbent dose, solution pH, contact time, and initial concentration of MB were found to affect adsorption efficiency. The kinetic data are well fitted with pseudo-second-order model with the adsorption rate of 0.002 g mg-1 min-1 and initial rate of 4.46 mg g-1 min-1. For the adsorption isotherm, three nonlinear models were used to analyze the experiment data, including Langmuir, Freundlich, and Temkin. The results indicate that the Langmuir model best describes the adsorption of Annona glabra L. seeds powder (AGSP) with a maximum adsorption capacity of 98.0 mg g-1. The investigation underpins the adsorption mechanism, whereby the electrostatic attraction between positively charged MB and negatively charged surface of AGSP is expected to be the predominant mechanism, together with hydrogen bonding and pi-pi interaction. These results make AGSP an interesting biosorbent concerning its environmental friendliness, cost-effectiveness, and relatively high dye adsorption capacity.

Total Synthesis of Cyanobactin Natural Product Balgacyclamide B.

Balgacyclamide A-C are a family of cyanobactin natural products isolated from freshwater cyanobacteria Microcystis aeruginosa. These macrocyclic peptides are characterized by their oxazoline-thiazole core, their 7 or 8 stereocenters, and their antiparasitic activities. Balgacyclamide B is known for its activity towards Plasmodium falciparum chloroquine-resistant strain K1, Trypanosoma brucei rhodesiense, and Leishmania donovani. In this report, the first total synthesis of Balgacyclamide B is described in a 17-steps pathway and a 2 % overall yield. The synthetic pathway toward balgacyclamide B can be adapted for the future syntheses of balgacyclamide A and C. In addition, a brief history background of oxazolines syntheses is shown to emphasize the importance of the cyclization conditions used to interconvert or retain configuration of ß-hydroxy amides via dehydrative cyclization.

Tinctorin, a new spiroterpenoid from the lichen Parmotrema tinctorum (Despr. ex Nyl.) Hale growing in Vietnam.

A new spiroterpenoid, namely tinctorin (1), along with one known compound, norreticulatin (2), were isolated from the lichen Parmotrema tinctorum (Despr. ex Nyl.) Hale. Their structures were elucidated by extensive spectroscopic analyses and electronic circular dichroism (ECD) calculations. The absolute configuration of 2 was established for the first time. Compound 1 was evaluated for its inhibitory activity against α-glucosidase and found to be inactive.

Endovascular management of severe lower gastrointestinal bleeding from angiodysplasia in the cecum: A case report.

Gastrointestinal angiodysplasias (GIADs) are rare disorder but can cause noticeable issue clinically. Their clinical characteristics can range from being an asymptomatic incidental finding to causing life-threatening bleeding. Many modalities are applied for treating bleeding GIADs include endoscopic therapies, angiography with embolization, surgical resection, and pharmacologic therapy. However, since patients with GIADs are often aged and have many comorbidities, endoscopic therapies may not be the best initial option. Angiography is suitable method for hemodynamically unstable patients with active bleeding, patients with an unknown active bleeding source, and patients who are poor surgical candidates. Angiography not only diagnose the bleeding point but also provide therapeutic endovascular intervention at the same time. We report a case of endovascular management of severe lower gastrointestinal bleeding from a GIAD in the cecum using a mixture of n-butyl cyanoacrylate and lipiodol to embolize the bleeding source. Clinical symptoms improved without prominent complications.

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey.

Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.

A Comparison of a Conventional Chemical Coagulant and a Natural Coagulant Derived from Cassia fistula Seeds for the Removal of Heavy Metal Ions.

Cassia fistula seed-derived coagulant has been reported to exhibit high coagulating-flocculating activity, environmental friendliness, and cost-effectiveness for the wastewater treatment, especially of textile wastewater. For heavy metal removal, however, research focusing on evaluating the feasibility of this material is still limited. Therefore, this study reports jar-test experiments in which the Zn2+ and Ni2+ removal efficiency of C. fistula coagulant was assessed. Moreover, a comparison of coagulation performance using a conventional chemical coagulant and the natural coagulant was performed. Characterization of the C. fistula seed-derived coagulant revealed the presence of important functional groups and fibrous networks with rough surfaces. A bench-scale study indicated that the coagulation performance of the two coagulants depends strongly on the initial concentration of metal ions, pH level, and coagulant dosage. The C. fistula seed-derived coagulant was found to possess higher removal efficiency than polyaluminum chloride. This natural coagulant removed over 80% of metal ions at the optimal conditions of pH 5.0, a metal ion concentration of 25 ppm, and a dosage of 0.8 and 1.6 g/L for Zn2+ and Ni2+, respectively. This study shows that C. fistula seed-derived coagulant is a potential alternative to chemical coagulants and could be developed to provide an environmentally friendly, economical, and efficient wastewater treatment.

Eumitrins I-K: three new xanthone dimers from the lichen Usnea baileyi.

In the continuing discovery and structure elucidation of natural xanthone dimers, which are still rarely reported in absolute configuration, three new xanthone dimers, eumitrins I-K (1-3) were isolated from the lichen Usnea baileyi, a rich source of natural xanthone dimers. Their structures were elucidated unambiguously by spectroscopic analyses, including high-resolution electrospray ionization mass spectrometry (HRESIMS), 1D and 2D nuclear magnetic resonance spectroscopy (1D and 2D NMR). The absolute configuration of all three compounds was established through DP4 probability and ECD calculation. All compounds revealed weak activity for their enzymatic inhibition against α-glucosidase and tyrosinase, as well as antibacterial activity.

New Cognitive Deep-Learning CAPTCHA.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks.

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.

Cost-Effective Network Reordering Using FPGA.

The advancement of complex Internet of Things (IoT) devices in recent years has deepened their dependency on network connectivity, demanding low latency and high throughput. At the same time, expanding operating conditions for these devices have brought challenges that limit the design constraints and accessibility for future hardware or software upgrades. These limitations can result in data loss because of out-of-order packets if the design specification cannot keep up with network demands. In addition, existing network reordering solutions become less applicable due to the drastic changes in the type of network endpoints, as IoT devices typically have less memory and are likely to be power-constrained. One approach to address this problem is reordering packets using reconfigurable hardware to ease computation in other functions. Field Programmable Gate Array (FPGA) devices are ideal candidates for hardware implementations at the network endpoints due to their high performance and flexibility. Moreover, previous research on packet reordering using FPGAs has serious design flaws that can lead to unnecessary packet dropping due to blocking in memory. This research proposes a scalable hardware-focused method for packet reordering that can overcome the flaws from previous work while maintaining minimal resource usage and low time complexity. The design utilizes a pipelined approach to perform sorting in parallel and completes the operation within two clock cycles. FPGA resources are optimized using a two-layer memory management system that consumes minimal on-chip memory and registers. Furthermore, the design is scalable to support multi-flow applications with shared memories in a single FPGA chip.

Tumour microbiomes and Fusobacterium genomics in Vietnamese colorectal cancer patients.

Perturbations in the gut microbiome have been associated with colorectal cancer (CRC), with the colonic overabundance of Fusobacterium nucleatum shown as the most consistent marker. Despite its significance in the promotion of CRC, genomic studies of Fusobacterium is limited. We enrolled 43 Vietnamese CRC patients and 25 participants with non-cancerous colorectal polyps to study the colonic microbiomes and genomic diversity of Fusobacterium in this population, using a combination of 16S rRNA gene profiling, anaerobic microbiology, and whole genome analysis. Oral bacteria, including F. nucleatum and Leptotrichia, were significantly more abundant in the tumour microbiomes. We obtained 53 Fusobacterium genomes, representing 26 strains, from the saliva, tumour and non-tumour tissues of six CRC patients. Isolates from the gut belonged to diverse F. nucleatum subspecies (nucleatum, animalis, vincentii, polymorphum) and a potential new subspecies of Fusobacterium periodonticum. The Fusobacterium population within each individual was distinct and in some cases diverse, with minimal intra-clonal variation. Phylogenetic analyses showed that within four individuals, tumour-associated Fusobacterium were clonal to those isolated from non-tumour tissues. Genes encoding major virulence factors (Fap2 and RadD) showed evidence of horizontal gene transfer. Our work provides a framework to understand the genomic diversity of Fusobacterium within the CRC patients, which can be exploited for the development of CRC diagnostic and therapeutic options targeting this oncobacterium.

Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture.

Yield estimation (YE) of the crop is one of the main tasks in fruit management and marketing. Based on the results of YE, the farmers can make a better decision on the harvesting period, prevention strategies for crop disease, subsequent follow-up for cultivation practice, etc. In the current scenario, crop YE is performed manually, which has many limitations such as the requirement of experts for the bigger fields, subjective decisions and a more time-consuming process. To overcome these issues, an intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19 (a deep learning-based semantic segmentation architecture). The dataset of 672 images was given as an input to the SegNet with VGG19 architecture for training. It extracts features corresponding to the tomato in each layer and detection was performed based on the feature score. The results were compared against the other semantic segmentation architectures such as U-Net and SegNet with VGG16. The proposed method performed better and unveiled reasonable results. For testing the trained model, a case study was conducted in the real tomato field at Manapparai village, Trichy, India. The proposed method portrayed the test precision, recall and F1-score values of 89.7%, 72.55% and 80.22%, respectively along with reasonable localization capability for tomatoes.

Fecal indicator bacteria diversity and decay in an estuarine mangrove ecosystem of the Xuan Thuy National Park, Vietnam.

Mangroves are complex and dynamic ecosystems that are highly dependent on diverse microbial activities. In this study, laboratory experiments and field studies for fecal indicator bacteria (FIB) decay rates are carried out for the first time in the Xuan Thuy Mangrove Forest Reserve of Vietnam. Results show that there are significant differences in bacterial diversity in the water of mangrove areas that have been deforested compared to those which have been planted. The highest mean total coliform (TC) and Escherichia coli (EC) values were found in the natural mangroves (3,807±2,922 and 964±1133 CFU 100 ml-1, respectively). The results indicated that the source of contamination and seasonal changes affect the abundance of fecal bacteria. These results were exceeding by far the safety guidelines for individual, non-commercial water supplies in most of the samples. In the planted mangrove sampling sites, the highest mean Fecal streptococci (FS) values of 1,520±1,652 CFU 100 ml-1 were found. Microbial die-off rates were calculated over 5 days, and observed to be systematically higher for TC than for EC.

A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition.

Human action recognition is an important field in computer vision that has attracted remarkable attention from researchers. This survey aims to provide a comprehensive overview of recent human action recognition approaches based on deep learning using RGB video data. Our work divides recent deep learning-based methods into five different categories to provide a comprehensive overview for researchers who are interested in this field of computer vision. Moreover, a pure-transformer architecture (convolution-free) has outperformed its convolutional counterparts in many fields of computer vision recently. Our work also provides recent convolution-free-based methods which replaced convolution networks with the transformer networks that achieved state-of-the-art results on many human action recognition datasets. Firstly, we discuss proposed methods based on a 2D convolutional neural network. Then, methods based on a recurrent neural network which is used to capture motion information are discussed. 3D convolutional neural network-based methods are used in many recent approaches to capture both spatial and temporal information in videos. However, with long action videos, multistream approaches with different streams to encode different features are reviewed. We also compare the performance of recently proposed methods on four popular benchmark datasets. We review 26 benchmark datasets for human action recognition. Some potential research directions are discussed to conclude this survey.

Impact of Music in Males and Females for Relief from Neurodegenerative Disorder Stress.

Neurological imbalance sometimes resulted in stress, which is experienced by the number of people at some moment in their life. A considerable measurement scheme can quantify the stress level in an individual, in which music has always been considered as the best therapy for stress relief in healthy human being as well in severe medical conditions. In this work, the impact of four types of music interventions with the lyrics of Hindi music and varying spectral centroid has been studied for an analysis of stress relief in males and females. The self-reported data for stress using state-trait anxiety (STA) and electroencephalography (EEG) signals for 14 channels in response to music interventions have been considered. Features such as Hjorth (activity, mobility, and complexity), variance, standard deviation, skew, kurtosis, and mean have been extracted from five bands (delta, theta, alpha, beta, and gamma) of each channel of the recorded EEG signals from 9 males and 9 females of the age category between 18 and 25 years. The support vector machine classifier has been used to classify three subsets: (i) male and female, (ii) baseline and female, and (iii) baseline and male. The noteworthy accuracy of 100% was found at the delta band for the first subset, beta and gamma bands for the second subset, and beta, gamma, and delta bands for the third subset. STA score has shown more deviation in the male category than in female, which gives a clear insight into the impact of music intervention with varying spectral centroid that has a higher impact to relieve stress in the male category than the female category.

Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.

Optimization of Mental Health-Related Critical Barriers in IoT-Based Teaching Methodology.

Online learning has changed all elements of teaching of entire learning structure from primary to university level all around the world so that the challenges of online teaching are required to be optimized. The prominent objective of this manuscript is to optimize the issues of online teaching-learning in online education. Twelve issues of online teaching-learning are shortlisted by performing deep reviewing of the literature and grouping into three categories: "Students' issues," "Common issues," and "Teachers' issues" using the opinions of expert people. The analytical hierarchy process method is chosen for ranking of issues of online teaching. The findings can become effective in planning to get solution of the challenges of online teaching. These challenges of online teaching may lead to fragmental illness mentally over a long period of time. Because social media platforms may become an efficient tool for incorporating into online education, social media is a vital aspect of online learning. Over time, social media use may have an effect on the human brain in one way or another. The given work's exploration of online teaching-learning challenges could lead to a social media-based examination of mental illness.