Chemoprofiling and medicinal potential of underutilized leaves of Cyperus scariosus.

Agro-waste is the outcome of the under-utilization of bioresources and a lack of knowledge to re-use this waste in proper ways or a circular economy approach. In the Indian medicinal system, the root of Cyperus scariosus (CS) is used at a large scale due to their vital medicinal properties. Unfortunately, the aerial part of CS is treated as agro-waste and is an under-utilized bioresource. Due to a lack of knowledge, CS is treated as a weed. This present study is the first ever attempt to explore CS leaves as medicinally and a nutrient rich source. To determine the food and nutritional values of the neglected part of Cyperus scariosus R.Br. (CS), i.e. CS leaves, phytochemicals and metal ions of CS were quantified by newly developed HPLC and ICPOES-based methods. The content of the phytochemicals observed in HPLC analysis for caffeic acid, catechin, epicatechin, trans-p-coumaric acid, and trans-ferulic acid was 10.51, 276.15, 279.09, 70.53, and 36.83 µg/g, respectively. In GC-MS/MS analysis, fatty acids including linolenic acid, phytol, palmitic acid, etc. were identified. In ICPOES analysis, the significant content of Na, K, Ca, Cu, Fe, Mg, Mn, and Zn was observed. The TPC and TFC of the CS leaves was 17.933 mg GAE eq./g and 130.767 mg QCE eq./g along with an IC50 value of 2.78 mg/mL in the DPPH assay and better antacid activity was measured than the standard (CaCO3). The methanolic extract of CS leaves showed anti-microbial activity against Staphylococcus aureus (15 ± 2 mm), Pseudomonas aeruginosa (12 ± 2 mm) and Escherichia coli (10 ± 2 mm). In silico studies confirmed the in vitro results obtained from the antioxidant, antiacid, and anti-microbial studies. In addition, in silico studies revealed the anti-cancerous and anti-inflammatory potential of the CS leaves. This study, thus, demonstrated the medicinal significance of the under-utilized part of CS and the conversion of agro-waste into mankind activity as a pharmaceutical potent material. Consequently, the present study highlighted that CS leaves have medicinal importance with good nutritional utility and have a large potential in the pharmaceutical industry along with improving bio-valorization and the environment.

Myiasis of open great toe fracture wound: a rare case report.

Introduction and importance: Myiasis of an open fracture wound is very rare but can occur due to neglect of wound care. Case presentation: A 12-year-old boy from a low socio-economic background, following an impact injury in his right great toe 10 days back presented with complaints of pain, swelling, and a foul-smelling odor from his right great toe. On examination, a swollen, tender puncture wound was noted over the dorsal aspect of the great toe revealing part of live larvae and serosanguinous discharge. Management was done with the complete removal of maggots, wound debridement, wound lavage, administration of systemic antibiotics, and toe guard slab application. Clinical discussion: Wound myiasis results from a facultative or obligatory parasite that is initiated when flies oviposit in hemorrhagic, necrotic, or pus-filled lesions. The possible complications of myiasis include local destruction, invasion into deep tissues, and secondary infection, which could result in amputation of the affected area, especially where obligatory parasites are concerned. Conclusion: Myiasis commonly occurs due to poor hygiene and neglect of wound care along with many other risk factors. Early proper wound care prevents the development of wound myiasis and early diagnosis and treatment of myiasis prevent complication of local tissue destruction and amputation of affected parts.

Quantification of Phytochemicals and Metal Ions as well as the Determination of Volatile Compounds, Antioxidant, Antimicrobial and Antacid Activities of the Mimosa pudica L. Leaf: Exploration of Neglected and Under-Utilized Part.

Mimosa pudica L. (MP) is well-known plant in traditional medicinal system, especially in India. Unfortunately, leaves of MP are less explored. To determine the food and nutritional value of the neglected part of Mimosa pudica L. (MP), that is MP leaves, phytochemicals and metal ions of MP were quantified by newly developed HPLC and ICPOES-based methods. The content of phytochemicals observed using HPLC analysis for chlorogenic acid, catechin, and epicatechin was 141.823 (±8.171), 666.621 (±11.432), and 293.175 (±12.743) µg/g, respectively. Using GC/MS/MS analysis, fatty acid like oleic acid were identified. In ICP-OES analysis, a significant content of Na, K, Ca, Cu, Fe, Mg, Mn, and Zn was observed. The observed TPC and TFC for MP leaf extracts was 44.327 (±1.041) mg GAE/ g of wt. and 214.217 (±4.372) mg QCE/ g of wt., respectively. The DPPH assay depicted a strong antioxidant activity of MP leaf extracts with IC50 values of 0.796 (±0.081) mg/mL and a TEAC value of 0.0356 (±0.0003). A significant antacid activity (666 mg MP+400 mg CaCO3 >400 mg CaCO3 ≫666 mg Gelusil) of MP leaves was noticed. The methanolic extract of MP leaves demonstrated anti-microbial activity against Staphylococcus aureus (15±2mm), Pseudomonas aeruginosa (12±2mm) and Escherichia coli (10±2mm). In silico studies confirmed the in vitro results obtained for antioxidant, antiacid, and anti-microbial activities. In addition, in silico studies revealed the anti-cancerous and anti-inflammatory potential of the MP leaves. In summary, this study demonstrated the medicinal significance of MP leaves and the conversion of agro-waste or the under-utilized part of MP into pharmaceutical potent materials. Consequently, the present study highlighted that MP leaves alone have medicinal importance with good nutritional utility and possess large promise in the pharma industry along with improving bio-valorization and the environment.

In-situ and airborne hyperspectral data for detecting agricultural activities in a dense forest landscape.

Maintaining rich biodiversity and being a habitat and resource for humans, tropical forests are one of the most important global biomes. These forest ecosystems have been experiencing a host of unregulated anthropogenic activities including illegal tourism, and shifting cultivation. The presence of human-habitats in the restricted zones of forest ecosystems is a direct indicator of the human activities that may accelerate deterioration of forest quality by area and tree species composition. Remote sensing data have been extensively used for mapping forest types, and biophysical characterization at various spatial scales. Several remote sensing datasets from multispectral, hyperspectral and LIDAR sensors are available for developing and validating a host of methodologies for remote sensing application in forestry. However, quantifying the quality of forest stands and detecting potential threats from the sporadic and small-scale human activities requires sub-pixel level remote sensing data analysis methods such as, spectral mixture modelling. Generally, most of the studies employ pixel-level supervised learning-based analysis techniques to detect infrastructure and settlements. However, if the settlements are smaller than the ground sampling distance and are under the canopy, pixel-based techniques are not suitable. Reinvigorated with progressive availability of hyperspectral imagery, spectral mixture modelling based sub-pixel image analysis is gaining prominence in the contemporary remote sensing application development. However, there is a paucity of high-resolution hyperspectral imagery and associated ground truth spectral measurements for assessing various methodological approaches on studies related to anthropogenic activities and forest disturbance. Most of the studies have relied upon simulating and synthesising the hyperspectral imagery and its associated ground truth spectra for implementation of methods and algorithms. This article presents a distinct dataset of high-resolution hyperspectral imagery and associated ground truth spectra of various vegetable crops acquired over a tropical forest ecosystem. The dataset is valuable for research on developing new discrimination models of forest and cultivated vegetation, classification methods, spectral matching analysis techniques, and sub-pixel target detection methods.

Quality-of-Service-Centric Design and Analysis of Unmanned Aerial Vehicles.

Recent years have witnessed rapid development and great indignation burgeoning in the unmanned aerial vehicles (UAV) field. This growth of UAV-related research contributes to several challenges, including inter-communication from vehicle to vehicle, transportation coverage, network information gathering, network interworking effectiveness, etc. Due to ease of usage, UAVs have found novel applications in various areas such as agriculture, defence, security, medicine, and observation for traffic-monitoring applications. This paper presents an innovative drone system by designing and developing a blended-wing-body (BWB)-based configuration for next-generation drone use cases. The proposed method has several benefits, including a very low interference drag, evenly distributed load inside the body, and less radar signature compared to the state-of-the-art configurations. During the entire procedure, a standard design approach was followed to optimise the BWB framework for next-generation use cases by considering the typically associated parameters such as vertical take-off and landing and drag and stability of the BWB. Extensive simulation experiments were performed to carry out a performance analysis of the proposed model in a software-based environment. To further confirm that the model design of the BWB-UAV is fit to execute the targeted missions, the real-time working environments were tested through advanced numerical simulation and focused on avoiding cost and unwanted wastages. To enhance the trustworthiness of this said computational fluid dynamics (CFD) analysis, grid convergence test-based validation was also conducted. Two different grid convergence tests were conducted on the induced velocity of the Version I UAV and equivalent stress of the Version II UAV. Finite element analysis-based computations were involved in estimating structural outcomes. Finally, the mesh quality was obtained as 0.984 out of 1. The proposed model is very cost-effective for performing a different kind of manoeuvring activities with the help of its unique design at reasonable mobility speed and hence can be modelled for high-speed-based complex next-generation use cases.

Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems.

Recent developments in the miniaturization of hyperspectral imaging sensors have given rise to the increased use of hyperspectral imagery as the primary data for evaluating spectral unmixing algorithms in applications such as industrial quality control, agriculture, mineral mapping, military, etc. This article presents an ultra-high-resolution hyperspectral imagery dataset for undertaking benchmark studies on spectral unmixing. A terrestrial hyperspectral imager (THI) is used for imaging the target scene with the camera sensor pointing horizontally towards the target scene. The datasets are acquired at various spatial resolutions ranging from 1 mm to 2 cm. The targeted scene contains several paper-based panels, each size of 2 cm x 2 cm and filled with different colours and proportions, glued to a black background board that maintains a distinguishable distance between one another. In addition to the hyperspectral imagery data acquisitions, reference spectral signatures of the candidate mixture materials are obtained by in-situ hyperspectral reflectance measurements using a spectroradiometer. The hyperspectral image acquisition and the in-situ spectral signatures of the target scene are collected under natural illumination conditions. The proposed datasets are designed for undertaking proof-of-the-concept (PoC) studies in spectral unmixing. The datasets are also valuable for evaluating the performance of different statistical and machine learning algorithms for target detection, classification, and sub-pixel classification algorithms.

Multi-platform optical remote sensing dataset for target detection.

Target detection in remote sensing has vital applications in mineral mapping, law enforcement, precision agriculture, strategic surveillance, etc. We present the acquisition of a first-of-its-kind high-resolution multi-platform (ground, airborne, and space-borne) remote sensing-based benchmark dataset for target detection studies. The dataset includes imagery acquired from terrestrial hyperspectral imager (THI), airborne hyperspectral sensor (AVIRIS-NG), and space-borne multi-spectral (Sentinel-2) sensor on 20th March 2018. Five engineered targets of different materials and colours were placed on different surface backgrounds. Besides, in-situ reflectance spectra of the targets were also acquired using a spectroradiometer for serving as a spectral reference source. The airborne and space-borne imagery were processed to remove un-calibrated/noisy bands and were atmospherically corrected using a radiative transfer method based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. The in-situ target reflectance spectra were resampled to spectrally match with airborne and space-borne imagery. Further, a target region of interest (ROI) was designated for each of the targets in both airborne and space-borne imagery using the known ground position of targets using a GPS device. This article provides a ground to space integrated target detection dataset, including ground positions ROI of the targets, point, and pixel-based in-situ target reference spectra, and the processed airborne and space-borne imagery to make the dataset ready for use. The data acquired in this experiment is an attempt to assess the potential of engineered material target detection in a multi-scale multi-platform view setup. The dataset is a valuable resource for testing and validation of target detection algorithms from various strategic and civilian application perspectives of remote sensing.

Ground state of interacting quantum magnetic dipoles: transition from a ferromagnetic Fermi liquid to an antiferromagnetic solid.

We show that the ground state of a system of magnetic dipoles, with no electric charge, is a ferromagnetic quantum Fermi liquid at high densities, driven by the dipolar exchange energy. As in the system of classical point dipoles, the direct dipole energy is zero in this case. With decreasing density, there is a transition to an antiferromagnetic lattice state. An addition of short range hard core repulsive potential will arrest the infinite density collapse of the ferromagnetic state, and possible melting of the low density antiferromagnetic lattice state.

Stability of the high-density ferromagnetic ground state of a chargeless, magnetic-dipolar, quantum Fermi liquid.

We obtain the best upper bound for the ground-state energy of a system of chargeless fermions of mass m, spin s=1/2 , and magnetic moment mus[over ] as a function of its density in the fully spin-polarized Hartree-Fock determinantal state, specified by a prolate spheroidal plane-wave single-particle occupation function n_(k[over ]) , by minimizing the total energy E at each density with respect to the variational spheroidal deformation parameter beta(2),0< or =beta(2)< or =1 . We find that at high densities, this spheroidal ferromagnetic state is the most likely ground state of the system, but it is still unstable towards the infinite-density collapse. This optimized ferromagnetic state is shown to be a stable ground state of the dipolar system at high densities, if one has an additional repulsive short-range hardcore interaction of sufficient strength and nonvanishing range.