Winter beans: the use of an unmanned aerial vehicle for monitoring and prediction of crop performance

Traditional field-based techniques for phenotyping of crops are based on visual assessment which are subjective and time consuming. A high throughput automated technique using an unmanned aerial vehicle (UAV) with a multispectral image (MSI) camera was used to investigate the correlation between markers of winter bean crop development with eventual crop yield. A simplified approach has been developed using different vegetation indices i.e. NDVI, GNDVI and NDRE, coupled with an iso-cluster classification method to monitor plant characteristics across all growing stages. The UAV-MSI data could then be incorporated into a yield estimator model to estimate the winter bean seed yield.  The NDVI approach showed the greatest correlation between the modelled seed yield and the actual seed yield determined on two separate occasions (R2 = 0.84 and R2 = 0.87). In addition, GNDVI and NDRE were a better estimator of seed yield for areas with dense vegetation. These are hence shown to be able to monitor a winter bean harvest in an efficient and timely manner

Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy

The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies

A strip of 20th-century landscape woodland planted alongside a 17th to mid-18th century ancient and semi-natural woodland (ASNW) was investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with a multispectral image camera (MSI). A simple classification approach of normalized difference spectral index (NDSI), derived using principal component analysis (PCA), enabled the identification of the non-native trees within the 20th-century boundary. The tree species within this boundary, classified by NDSI, were further segmented by the machine learning segmentation method of k-means clustering. This combined innovative approach has enabled the identification of multiple tree species in the 20th-century boundary. Phenotyping of trees at canopy level using the UAV with MSI, across 8052 m2, identified black pine (23), Norway maple (19), Scots pine (12), and sycamore (19) as well as native trees (oak and silver birch, 27). This derived data was corroborated by field identification at ground-level, over an area of 6785 m2, that confirmed the presence of black pine (26), Norway maple (30), Scots pine (10), and sycamore (14) as well as other trees (oak and silver birch, 20). The benefits of using a UAV, with an MSI camera, for monitoring tree boundaries next to a new housing development are demonstrated

COVID-19: Is There Evidence for the Use of Herbal Medicines as Adjuvant Symptomatic Therapy?

Background: Current recommendations for the self-management of SARS-Cov-2 disease (COVID-19) include self-isolation, rest, hydration, and the use of NSAID in case of high fever only. It is expected that many patients will add other symptomatic/adjuvant treatments, such as herbal medicines. Aims: To provide a benefits/risks assessment of selected herbal medicines traditionally indicated for “respiratory diseases” within the current frame of the COVID-19 pandemic as an adjuvant treatment. Method: The plant selection was primarily based on species listed by the WHO and EMA, but some other herbal remedies were considered due to their widespread use in respiratory conditions. Preclinical and clinical data on their efficacy and safety were collected from authoritative sources. The target population were adults with early and mild flu symptoms without underlying conditions. These were evaluated according to a modified PrOACT-URL method with paracetamol, ibuprofen, and codeine as reference drugs. The benefits/risks balance of the treatments was classified as positive, promising, negative, and unknown. Results: A total of 39 herbal medicines were identified as very likely to appeal to the COVID-19 patient. According to our method, the benefits/risks assessment of the herbal medicines was found to be positive in 5 cases (Althaea officinalis, Commiphora molmol, Glycyrrhiza glabra, Hedera helix, and Sambucus nigra), promising in 12 cases (Allium sativum, Andrographis paniculata, Echinacea angustifolia, Echinacea purpurea, Eucalyptus globulus essential oil, Justicia pectoralis, Magnolia officinalis, Mikania glomerata, Pelargonium sidoides, Pimpinella anisum, Salix sp, Zingiber officinale), and unknown for the rest. On the same grounds, only ibuprofen resulted promising, but we could not find compelling evidence to endorse the use of paracetamol and/or codeine. Conclusions: Our work suggests that several herbal medicines have safety margins superior to those of reference drugs and enough levels of evidence to start a clinical discussion about their potential use as adjuvants in the treatment of early/mild common flu in otherwise healthy adults within the context of COVID-19. While these herbal medicines will not cure or prevent the flu, they may both improve general patient well-being and offer them an opportunity to personalize the therapeutic approaches

Use of unmanned aerial vehicle for environmental monitoring purposes and precision agriculture

Despite numerous research studies on remote sensing applications in forestry and precision agriculture, there is a limited availability of image analysis techniques that are less complex, reproducible, and applicable to diverse locations and under a wide range of environmental conditions. Image analysis techniques currently in use employ complex machine learning approaches (regression-based models), for example, to identify tree species in forestry and estimate crop yield in precision agriculture. However, many challenges must be overcome before these modern machine learning approaches can potentially see widespread commercial and non-commercial implementation in agriculture and forestry. As a result, there is a need to investigate and develop simple, dependable, and reproducible image analysis methods by utilising remote sensing data applicable in forestry and precision agriculture. Hence, the current study focuses on using a remote sensing platform of multispectral unmanned aerial vehicle (UAV) to monitor native and invasive tree species in an ancient semi-natural woodland and investigate the performance of a variety of crops for precision agriculture, including oilseed rape, winter beans, and winter oats. The multispectral UAV data were analysed using simple yet effective image analysis techniques such as principal component analysis (PCA), spectral vegetation indices combined with image classification methods of thresholding and clustering (k-means and iso-cluster). Also, the image analysis methods were performed with effective data manipulation software such as MATLAB and ArcGIS. Identification and quantification of native and invasive tree species was achieved by PCA derived spectral vegetation indices, thresholding and k-means clustering. Additionally, the use of spectral vegetation indices and iso-cluster classification method in precision agriculture of crops assisted in estimation of crop yield three months before harvest. Also, strong correlation was observed between the estimate and actual crop yield. Furthermore, a pilot study using a multinomial logistic regression model with high sensitivity and accuracy enabled the identification of soil nutrient concentration and crop quality features for very high oats yield. The simple and effective image analysis methods on multispectral UAV data for forestry and precision agriculture must be employed more frequently than complex machine learning approaches. Also, the estimated crop yield prior to harvesting aids farmers for precision agriculture of crops to maintain its performance. Whereas, in forestry these methods can be employed frequently to monitor the native tree species and emergence of new invasive tree species and remove them effectively to maintain a sustainable ecosystem

Adolescent Male Reproductive Health Knowledge and Practices in Bangladesh

Abstract: Opinions on reproductive health education at the onset of puberty at present were studied by using a structured questionnaire consisting of 13 questions with a view to know their conception about it. A total of 800 male students were randomly selected of which 400 were from two public and the rest 400 from private universities situated in Dhaka, Bangladesh. At least half of the university students (384, 48%) did not understand much about puberty and remained confused. A large number of adolescents felt shy (208, 26%), scared (56, 7%), least bothered (112, 14%) and were not at all aware (40, 5%) of their onset of puberty. The respondents reported to have discussion their pubertal changes mainly with their male peers (672, 84%) and a very little access to parents (16, 2%) and elder brothers (16, 2%). A few respondents talked with their teachers (40, 5%). Their shared feelings were not informative and rather incorrect for maintaining good reproductive health at a growing time. On the contrary, they were rather warned by the persons not to disclose it to others. Most of the respondents (672, 84%) felt sex education is essential for better reproductive health management, a few of them (88, 11%) opposed this idea and some of them remained silent (40, 5%). Half of the respondents (760, 50%) preferred reproductive health education should be included in secondary and higher secondary levels (375, 25%), in the university level (166, 11%) and very few wanted it to be included in primary level (93, 6%). A few number of respondents (92, 6%) preferred non-formal reproductive health education. Some of the respondents (785, 23%) wanted to learn through curriculum and discussion with partners o

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative “omics” approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis