Evaluation of decadal land degradation dynamics in old coal-mines of Central India

The present study attempts to understand land use dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur district in Madhya Pradesh, India through geospatial techniques. Land Use and Land Cover (LULC) change detection analysis was performed digitally classifying Landsat 5 (2001) as well as Landsat 8 (2020) satellite data using maximum likelihood algorithm. Results revealed that area under Dense native vegetation decreased drastically (13.74 sq. km) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 sq. km). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico-chemical parameters. Studies suggested soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty-year time-scale indicates that the reclamation activities are still in its’ infancy. Land Degradation Vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60 % of the area is highly vulnerable to degradation. The LDI inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001-2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.

Analyze the usage of urban greenways through social media images and computer vision

Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV) technology to analyze and compare how the general public uses two typical greenway parks, The High Line in New York City and the Atlanta Beltline in Atlanta. Face and object detection analysis were conducted to infer user composition, activities, and key experiences. We presented the temporal patterns of Instagram postings as well as the group gatherings, smiling, and representative objects detected from photos. Our results have shown high user engagement levels for both parks while teens are significantly underrepresented. The High Line had more group activities and was more active during weekdays than the Atlanta Beltline. Stronger sense of escape and physical activities can be found in Atlanta Beltline. In summary, social media images like Instagram can provide strong empirical evidence for urban greenway usage when combined with artificial intelligence technologies, which can support the future practice of landscape architecture and urban design.

Scientometric Analysis of Public Health Emergencies: 1994–2020

The purpose of this paper is to summarize the research hotspots and frontiers in the field of public health emergencies (PHE) between 1994–2020 through the scientometric analysis method. In total, 2247 literature works retrieved from the Web of Science core database were analyzed by CiteSpace software, and the results were displayed in knowledge mapping. The overall characteristics analysis showed that the number of publications and authors in the field of PHE kept an upward trend during the past decades, and the United States was in the leading position, followed by China and England. Switzerland has the highest central value and plays an important intermediary role in promoting the integration and exchange of international PHE research achievements. The keyword co-occurrence analysis indicated that COVID-19 was the most high-frequency keyword in this field, and there had been no new keywords for a long time until the outbreak of COVID-19 in 2019. The burst detection analysis showed that the top five burst keywords in terms of burst intensity were zika virus, Ebola, United States, emergency preparedness and microcephaly. The results indicated that the research theme of PHE is closely related to the major infectious diseases in a specific period. It will continue to develop with more attention paid to public health. The conclusions can provide help and reference for the PHE potential researchers.

First report of fatty acids in Mimosadiplotricha bee pollen with in vitro lipase inhibitory activity

Bee pollen (BP) is full of nutrients and phytochemicals, and so it is widely used as a health food and alternative medicine. Its composition and bioactivity mainly depend on the floral pollens. In this work, BP collected by Apis mellifera with different monoculture flowering crops (BP1-6) were used. The types of floral pollen in each BP were initially identified by morphology, and subsequently confirmed using molecular phylogenetic analysis. Data from both approaches were consistent and revealed each BP to be monofloral and derived from the flowers of Camellia sinensis L., Helianthus annuus L., Mimosa diplotricha, Nelumbo nucifera, Xyris complanata, and Ageratum conyzoides for BP1 to BP6, respectively. The crude extracts of all six BPs were prepared by sequential partition with methanol, dichloromethane (DCM), and hexane. The crude extracts were then tested for the in vitro (i) α-amylase inhibitory, (ii) acetylcholinesterase inhibitory (AChEI), and (iii) porcine pancreatic lipase inhibitory (PPLI) activities in terms of the percentage enzyme inhibition and half maximum inhibitory concentration (IC50). The DCM partitioned extract of X. complanata BP (DCMXBP) had the highest active α-amylase inhibitory activity with an IC50 value of 1,792.48 ± 50.56 µg/mL. The DCM partitioned extracts of C. sinensis L. BP (DCMCBP) and M. diplotricha BP (DCMMBP) had the highest PPLI activities with an IC50 value of 458.5 ± 13.4 and 500.8 ± 24.8 µg/mL, respectively), while no crude extract showed any marked AChEI activity. Here, the in vitro PPLI activity was focused on. Unlike C. sinensis L. BP, there has been no previous report of M. diplotricha BP having PPLI activity. Hence, DCMMBP was further fractionated by silica gel 60 column chromatography, pooling fractions with the same thin layer chromatography profile. The pooled fraction of DCMMBP2-1 was found to be the most active (IC50 of 52.6 ± 3.5 µg/mL), while nuclear magnetic resonance analysis revealed the presence of unsaturated free fatty acids. Gas chromatography with flame-ionization detection analysis revealed the major fatty acids included one saturated acid (palmitic acid) and two polyunsaturated acids (linoleic and linolenic acids). In contrast, the pooled fraction of DCMMBP2-2 was inactive but pure, and was identified as naringenin, which has previously been reported to be present in M. pigra L. Thus, it can be concluded that naringenin was compound marker for Mimosa BP. The fatty acids in BP are nutritional and pose potent PPLI activity.

Metabolomic and transcriptomic analysis of Lycium chinese and L. ruthenicum under salinity stress

Background High soil salinity often adversely affects plant physiology and agricultural productivity of almost all crops worldwide, such as the crude drug known as wolfberry. However, the mechanism of this action in wolfberry is not fully understood yet. Results Here in this study, we studied different mechanisms potentially in Chinese wolfberry (Lycium chinese, LC) and black wolfberry (L. ruthenicum, LR) under salinity stress, by analyzing their transcriptome, metabolome, and hormone changes. The hormone detection analysis revealed that the ABA content was significantly lower in LR than LC under normal condition, and increased sharply under salinity stress in LR but not in LC. The transcriptome analysis showed that the salinity-responsive genes in wolfberry were mainly enriched in MAPK signaling, amino sugar and nucleotide sugar metabolism, carbon metabolism, and plant hormone signal transduction pathways in LC, while mainly related to carbon metabolism and protein processing in endoplasmic reticulum in LR. Metabolome results indicated that LR harbored higher flavone and flavonoid contents than LC under normal condition. However, the flavone and flavonoid contents were hardly changed in LR, but increased substantially in LC when exposed to salinity stress. Conclusions Our results adds ABA and flavone to mechanism understanding of salinity tolerance in wolfberry. In addition, flavone plays a positive role in resistance to salinity stress in wolfberry.

Integrated Metabolomic and Transcriptomic Analysis of the Anthocyanin and Proanthocyanin Regulatory Networks in Red Walnut Natural Hybrid Progeny Leaves

BackgroundWalnuts are one of the most important dry fruit crops worldwide, typically exhibiting green leaves and yellow–brown or gray–yellow seed coats. A specific walnut type, red walnut ‘RW-1’, with red leaves and seed coats was selected as the plant material because of its higher anthocyanin and proanthocyanin (PA) contents. Anthocyanins and PAs coprise important secondary defense methods for plants to respond to biotic and abiotic stresses. However, few studies have focused on the molecular mechanism of anthocyanin biosynthesis in walnuts.ResultsFrom the results of widely targeted metabolome and anthocyanidin detection analysis, 395 substances, including 4 PAs and 26 anthocyanins, were identified from the red-leaf walnuts of RW-1 natural hybrid progenies (SR) and the green-leaf walnuts of RW-1 natural hybrid progenies (SG). Among these, all anthocyanin types in SR were significantly upregulated compared with SG. Additionally, delphinidin 3-O-galactoside, cyanidin 3-O-galactoside, delphinidin 3-O-glucoside and cyanidin 3-O-glucoside were identified as the primary components of anthocyanidins because of their higher contents. Nine anthocyanidins, malvidin 3-O-galactoside, malvidin 3-O-arabinoside, cyanidin 3-O-(6-O-malonyl-beta-D-glucoside), delphinidin 3-O-glucoside, delphinidin 3,5-O-diglucoside (Delphin), peonidin 3-O-(6-O-malonyl-beta-D-glucoside), petunidin 3-O-(6-O-malonyl-beta-D-glucoside), petunidin 3-O-arabinoside and pelargonidin 3-O-(6-O-malonyl-beta-D-glucoside), were detected only in the SR walnuts. For PAs, proanthocyanin C1 was upregulated in SR compared with SG, while proanthocyanin B1 and proanthocyanin B3 were upregulated in SR-1 and SR-3 but downregulated in SR-2 compared with the controls. Furthermore, transcriptome analysis demonstrated that the expression of structural genes (C4H, F3H, F3’5’H, UFGTs, LAR and ANR), four MYBs and six WD40s in the anthocyanin and PA biosynthetic pathways were significantly higher in the SR walnut.ConclusionsOur results provide valuable information on anthocyanin and PA metabolites and candidate genes in anthocyanin and PA biosynthesis, which provides new insights into anthocyanin and PA biosynthesis in walnuts.

Fabrication of graphene–ZnO heterostructure-based flexible and thin platform-based UV detector

This work presents the performance evaluation of Graphene/ZnO Schottky junctions grown on flexible indium tin oxide (ITO)-coated polyethylene terephthalate (PET) substrates. The fabricated structures include chemical vapour deposition grown graphene layer on ITO-coated PET substrates. Polymethyl methacrylate assisted transfer method has been employed for the successful transfer of graphene from Cu substrate to PET. The smaller D-band intensity (1350 cm−1) compared to G-band (1580 cm−1) indicates good quality of carbon lattice with less number of defects. High-quality ZnO has been deposited through RF sputtering. The deposited ZnO with grain size 50–95 nm exhibited dislocation densities of 1.31270 × 10–3 nm−2 and compressive nature with negative strain of − 1.43156 GPa. Further, the electrical and optical characterization of the devices has been done through device I–V characterization and UV detection analysis. The UV detection capability of the device has been carried out with the aid of a UV-lamp of 365 nm wavelength. The fabricated graphene/ZnO photodetector showed good response to UV illumination. The device performance analysis has been done through a comparison of the device responsivity and detectivity with the existing detectors. The detectivity and responsivity of the fabricated detectors were 7.106 × 109 mHz1/2 W−1 and 0.49 A W−1, respectively.

A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations

The manual detection, analysis, and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups, and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighborhood-based dimensionality reduction of spectrograms to produce a latent-space representation of calls stands out for its conceptual simplicity and effectiveness. Using a dataset of manually annotated meerkat (Suricata suricatta) vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyze strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabeled calls. All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations.

QUANTIFYING FACIAL EXPRESSION USING ARTIFICIAL INTELLIGENCE IN CLASS 1 MOLAR RELATIONSHIP: A NOVEL RESEARCH PROTOCOL

Precise face detection analysis is a crucial element for a social interaction review. To the viewer, producing the facial features that correspond to the thoughts and feelings which succeed in arousing the sensation or enhancing of the emotional sensitivity. The study is based on Virtual Reality (VR), to evaluate facial expression using Azure Kinect in adults with Class I molar relationship. The study will be conducted in Human Research Lab, on participants with Class I molar relationship, by using Azure Kinect. 196 participants will be selected of age above 18 as per the eligibility criteria. This research would demonstrate the different tools and applications available by testing their precision and relevance to determine the facial expressions.