The Fungal Genus Chaetomium and Its Agricultural Applications

2020 ◽  
pp. 289-308
Author(s):  
Paulina Moya ◽  
Josefina Cipollone ◽  
Marina Sisterna
Keyword(s):  
Agricultural Applications ◽  
Fungal Genus

The endophytic fungi pestalotiopsis what’s for it and what’s on it?

2020 ◽  
Vol 4 (1) ◽  
pp. 01-05
Author(s):  
Waill Elkhateeb
Keyword(s):  
Secondary Metabolites ◽  
Endophytic Fungi ◽  
Biological Activities ◽  
Chemical Diversity ◽  
Healthy Life ◽  
Natural Substances ◽  
Agricultural Applications ◽  
Unique Chemical ◽  
Economical Losses ◽  
Fungal Genus

Fungi generally and endophytic ones specifically represent future factories and potent biotechnological tools for production of bioactive natural substances, which could extend healthy life of humanity. Pestalotiopsis is a fungal genus that belongs to family; Sporocadaceae, has been known as a promising secondary metabolites producer. However the same fungus showed harmful pathogenicity against different plants causing crops economical losses. This review is to demonstrate secondary metabolites from the endophytic fungi Pestalotiopsis and some of its reported biological activities. Moreover, describing the unique chemical diversity of this fungal genus involved in medical, pharmaceutical, agricultural applications. Also highlight the harmful side of this important fungus.

Konsumsi Energi Pada Alat Pengering Surya dan Tipe Bak Untuk Pengeringan Biji Kakao

2019 ◽  
Vol 17 (3) ◽  
Author(s):  
Lamhot P. Manalu
Keyword(s):  
Specific Energy ◽  
Fossil Fuels ◽  
Negative Impact ◽  
Solar Irradiation ◽  
Drying Process ◽  
Food Industries ◽  
Solar Dryer ◽  
Agricultural Applications ◽  
Drying System ◽  
Drying Efficiency

Crop drying is essential for preservation in agricultural applications. It is performed either using fossil fuels in an artificial mechanical drying process or by placing the crop under the open sun. The first method is costly and has a negative impact on the environment, while the second method is totally dependent on the weather. The drying process requires a lot of energy in relation to the amount of water that must be evaporated from the product. It is estimated that 12% of the total energy used by the food industries and agriculture absorbed in this process. Due to the limitation of energy resources, it is important to keep researching and developing of diversification and optimization of energy This study aims to assess the use of energy for cocoa drying using solar energy dryer and bin-type dryer, as well as to determine the drying efficiency of each type of dryer. The results showed that the efficiency of the solar dryer drying system ranges between 36% to 46%, while the tub-type dryers between 21.7% to 33.1%. The specific energy of solar dryer ranged from 6.17-7.87 MJ / kg, while the tub-type dryers 8.58-13.63 MJ / kg. Dryer efficiency is influenced by the level of solar irradiation and the amount of drying load, the higher the irradiation received and more cocoa beans are dried, the drying efficiency is also higher and the specific energy further down.Proses pengeringan memerlukan banyak energi sehubungan dengan banyaknya air yang harus diuapkan dari bahan yang dikeringkan. Pengeringan dapat dilakukan dengan menggunakan pengering mekanis berbahan bakar fosil atau dengan menempatkan produk di bawah matahari terbuka. Metode pertama adalah mahal dan memiliki dampak negatif pada lingkungan, sedangkan metode kedua sangat tergantung pada cuaca. Diperkirakan bahwa 12% dari total energi yang dipergunakan oleh industri pangan dan pertanian diserap untuk proses ini. Mengingat semakin terbatasnya sumber energi bahan bakar minyak maka usaha diversifikasi dan optimasi energi untuk pengeringan perlu terus diteliti dan dikembangkan. Salah satunya adalah pemanfaatan energi surya sebagai sumber energi terbarukan. Penelitian ini bertujuan untuk mengkaji penggunaan energi untuk pengeringan kakao dengan memakai pengering energi surya dan pengering tipe bak, serta untuk mengetahui efisiensi pengeringan dari masing-masing tipe pengering. Hasil kajian menunjukkan bahwa efisiensi total sistem pengeringan alat pengering surya berkisar antara 36% dan 46%, sedangkan pengering tipe bak antara 21.7% dan 33.1%. Kebutuhan energi spesifik alat pengering surya berkisar antara 6.17-7.87 MJ/kg, sedangkan alat pengering tipe bak 8.58-13.63 MJ/kg. Efisiensi alat pengering dipengaruhi oleh tingkat iradiasi surya dan jumlah beban pengeringan, semakin tinggi iradiasi yang diterima pengering serta semakin banyak biji kakao yang dikeringkan, maka efisiensi pengeringan juga semakin tinggi dan kebutuhan energi spesifik semakin turun.Keywords: energy, efficiency, cocoa, solar dryer, bin-type dryer.

scholarly journals Tracking of Zinc Ferrite Nanoparticle Effects on Pea (Pisum sativum L.) Plant Growth, Pigments, Mineral Content and Arbuscular Mycorrhizal Colonization

Plants ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 583
Author(s):  
Reda E. Abdelhameed ◽  
Nagwa I. Abu-Elsaad ◽  
Arafat Abdel Hamed Abdel Latef ◽  
Rabab A. Metwally
Keyword(s):  
Pisum Sativum ◽  
Plant Growth ◽  
Zinc Ferrite ◽  
Crop Improvement ◽  
Nanocrystalline Structure ◽  
Arbuscular Mycorrhizal ◽  
Am Fungi ◽  
Transmission Electron ◽  
Agricultural Applications ◽  
The Impact

Important gaps in knowledge remain regarding the potential of nanoparticles (NPs) for plants, particularly the existence of helpful microorganisms, for instance, arbuscular mycorrhizal (AM) fungi present in the soil. Hence, more profound studies are required to distinguish the impact of NPs on plant growth inoculated with AM fungi and their role in NP uptake to develop smart nanotechnology implementations in crop improvement. Zinc ferrite (ZnFe2O4) NPs are prepared via the citrate technique and defined by X-ray diffraction (XRD) as well as transmission electron microscopy for several physical properties. The analysis of the XRD pattern confirmed the creation of a nanocrystalline structure with a crystallite size equal to 25.4 nm. The effects of ZnFe2O4 NP on AM fungi, growth and pigment content as well as nutrient uptake of pea (Pisum sativum) plants were assessed. ZnFe2O4 NP application caused a slight decrease in root colonization. However, its application showed an augmentation of 74.36% and 91.89% in AM pea plant shoots and roots’ fresh weights, respectively, compared to the control. Moreover, the synthesized ZnFe2O4 NP uptake by plant roots and their contents were enhanced by AM fungi. These findings suggest the safe use of ZnFe2O4 NPs in nano-agricultural applications for plant development with AM fungi.

Essential Oils‐loaded Bio‐nanomaterials for Sustainable Agricultural Applications

2021 ◽  
Author(s):  
Matías Menossi ◽  
Romina P. Ollier ◽  
Claudia A. Casalongué ◽  
Vera A. Alvarez
Keyword(s):  
Essential Oils ◽  
Agricultural Applications

Toward fulfilling the robotic farming vision: advances in sensors and controllers for agricultural applications

2000 ◽  
Vol 36 (4) ◽  
pp. 1026-1032 ◽  
Author(s):  
T.K. Hamrita ◽  
E.W. Tollner ◽  
R.L. Schafer
Keyword(s):  
Agricultural Applications

scholarly journals Microbial Associations with Pancreatic Cancer: A New Frontier in Biomarkers

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3784
Author(s):  
Mark Stasiewicz ◽  
Marek Kwaśniewski ◽  
Tomasz M. Karpiński
Keyword(s):  
Pancreatic Cancer ◽  
Helicobacter Pylori ◽  
Survival Rates ◽  
Health Concern ◽  
Current State ◽  
Increased Risk ◽  
Microbial Biomarkers ◽  
New Frontier ◽  
Microbial Associations ◽  
Fungal Genus

Pancreatic cancer (PC) remains a global health concern with high mortality and is expected to increase as a proportion of overall cancer cases in the coming years. Most patients are diagnosed at a late stage of disease progression, which contributes to the extremely low 5-year survival rates. Presently, screening for PC remains costly and time consuming, precluding the use of widespread testing. Biomarkers have been explored as an option by which to ameliorate this situation. The authors conducted a search of available literature on PubMed to present the current state of understanding as it pertains to the use of microbial biomarkers and their associations with PC. Carriage of certain bacteria in the oral cavity (e.g., Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, Streptococcus sp.), gut (e.g., Helicobacter pylori, Synergistetes, Proteobacteria), and pancreas (e.g., Fusobacterium sp., Enterobacteriaceae, Pseudomonadaceae) has been associated with an increased risk of developing PC. Additionally, the fungal genus Malassezia has likewise been associated with PC development. This review further outlines potential oncogenic mechanisms involved in the microbial-associated development of PC.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

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