Chemical Stability of Ascorbic Acid Integrated into Commercial Products: A Review on Bioactivity and Delivery Technology

The L-enantiomer of ascorbic acid is commonly known as vitamin C. It is an indispensable nutrient and plays a key role in retaining the physiological process of humans and animals. L-gulonolactone oxidase, the key enzyme for the de novo synthesis of ascorbic acid, is lacking in some mammals including humans. The functionality of ascorbic acid has prompted the development of foods fortified with this vitamin. As a natural antioxidant, it is expected to protect the sensory and nutritional characteristics of the food. It is thus important to know the degradation of ascorbic acid in the food matrix and its interaction with coexisting components. The biggest challenge in the utilization of ascorbic acid is maintaining its stability and improving its delivery to the active site. The review also includes the current strategies for stabilizing ascorbic acid and the commercial applications of ascorbic acid.

An Open-Source Low-Cost Imaging System Plug-In for Pheromone Traps Aiding Remote Insect Pest Population Monitoring in Fruit Crops

This note describes the development of a plug-in imaging system for pheromone delta traps used in pest population monitoring. The plug-in comprises an RGB imaging sensor integrated with a microcontroller unit and associated hardware for optimized power usage and data capture. The plug-in can be attached to the top of a modified delta trap to realize periodic image capture of the trap liner (17.8 cm × 17.8 cm). As configured, the captured images are stored on a microSD card with ~0.01 cm2 pixel−1 spatial resolution. The plug-in hardware is configured to conserve power, as it enters in sleep mode during idle operation. Twenty traps with plug-in units were constructed and evaluated in the 2020 field season for codling moth (Cydia pomonella) population monitoring in a research study. The units reliably captured images at daily interval over the course of two weeks with a 350 mAh DC power source. The captured images provided the temporal population dynamics of codling moths, which would otherwise be achieved through daily manual trap monitoring. The system’s build cost is about $33 per unit, and it has potential for scaling to commercial applications through Internet of Things-enabled technologies integration.

The N-terminal tail of the hydrophobin SC16 is not required for rodlet formation

Hydrophobins are small proteins that are secreted by fungi, accumulate at interfaces, modify surface hydrophobicity, and self-assemble into large amyloid-like structures. These unusual properties make hydrophobins an attractive target for commercial applications as green emulsifiers and surface modifying agents. Hydrophobins have diverse sequences and tertiary structures, and depending on the hydrophobin, different regions of their structure have been proposed to be required for self-assembly. To provide insight into the assembly process, we determined the first crystal structure of a class I hydrophobin, SC16. Based on the crystal structure, we identified a putative intermolecular contact that may be important for rodlet assembly and was formed in part by the N-terminal tail of SC16. Surprisingly, removal of the N-terminal tail did not influence the self-assembly kinetics of SC16 or the morphology of its rodlets. These results suggest that other regions of this hydrophobin class are required for rodlet formation and indicate that the N-terminal tail of SC16 is amenable to modification so that functionalized hydrophobin assemblies can be created.

A review on the use of nanomaterials in agriculture: benefits and associated health risks

Background: The present world population is about 7.9 billion and it is increasing continuously. Thus, there is an urgent requirement to enhance the agricultural output sustainably. Agricultural approaches such as the use of advanced agriculture methods, high productivity varieties, and enhanced application of fertilizers and pesticides have significantly increased food grain production but in an unsustainable way. Chemical-based conventional fertilizers and pesticides have been found associated with environmental pollution and other unwanted effects on the ecosystem, soil quality, and soil microflora, etc. Nanomaterials may be used to replace conventional fertilizers and pesticides in agriculture. Objective: The aim of this review is to provide information about the harmful effects of chemical fertilizers and pesticides, and the use of nanomaterials in agriculture. Including this, the health risks of nanomaterials are discussed. Method: This review article includes a survey of literature from different online sources (for example, Web of Science, PubMed, and Google Scholar, etc.). Results: The improvement in agricultural output using chemical fertilizers and pesticides is considered unsustainable as it is increasing the cost of production, affecting the soil quality, disturbing nutrient availability in crops, and causing environmental pollution. Nanotechnology is a potent innovative practice and nanomaterials may be used in agriculture as nanofertilizers, nanopesticides, and nanosensors. Although these approaches have the potential to enhance agricultural productivity in a sustainable way, nanomaterials are also assumed to exhibit potential health risks to humans. Reports have indicated that nanomaterials have been found associated with many systematic diseases such as cardiovascular diseases, neurotoxicity, and toxicity to the reproductive system, etc. Conclution: It is well accepted that chemical fertilizers and pesticides in agriculture cause environmental toxicity and affect ecosystem activity. Nanomaterials have the potential to enhance agricultural output, but these are also associated with health risks. Thus, detailed scientific studies must be conducted about the potential health risk of nanomaterials before their commercial applications in agriculture.

Comparative Studies on the Behaviour of Flat Panels Made of GRP Under Static and Dynamic Loads

Glass reinforced plastic, so called GRP, is a composite material made of glass strands called fibbers woven together to create a flexible fabric. GRP is a lightweight material with many and diverse applications ranging from the manufacture of reservoirs for different liquids to the manufacture of boats, yachts, chairs and even children playground furniture. The behaviour of this material under static and dynamic loads is still raising interest from the scientific community and a large number of researchers. This continued interest is due to the material versatility for different applications depending on its manufacture process that has a significant weigh-in in the material mechanical properties. These resulting mechanical properties need to be carefully analysed and benchmarked prior to using the obtained material in commercial applications. The scope of this research study is to analyse the behaviour of glass reinforced plastic plate panel with reinforcements on one and two directions under static and dynamic loads employing both experimental and numerical methods for results validation. The methods used in this research study for the dynamic loads can also be applied successfully to other composite materials. Additionally, the stress plots have been analysed in iteration in order to ensure the most optimal reinforcement pattern.

Analysis and Comparison of Electromagnetic Microrobotic Platforms for Biomedical Applications

Magnetic microrobotics is a promising technology for improving minimally invasive surgery (MIS) with the ambition of enhancing patient care and comfort. The potential benefits include limited incisions, less hemorrhaging and postoperative pain, and faster recovery time. To achieve this, a key issue relies on the design of a proper electromagnetic actuation (EMA) setup which is based on the use of magnetic sources. The magnetic field and its gradient generated by the EMA platform is then used to induce magnetic torque and force for microrobot manipulations inside the human body. Like any control systems, the EMA system must be adapted to the given controlled microrobot and customized for the application. With great research efforts on magnetic manipulating of microrobots, the EMA systems are approaching commercial applications, and their configurations are becoming more suitable to be employed in real medical surgeries. However, most of the proposed designs have not followed any specific rule allowing to take into account the biomedical applications constraints. Through reviewing the different proposed EMA systems in the literature, their various specifications and configurations are comprehensively discussed and analyzed. This study focus on EMA platforms that use electromagnets. From this review and based on the biomedical application specifications, the appropriate EMA system can be determined efficiently.

Neural Network for Big Data Sets

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

Biodegradation of petroleum by bacteria isolated from fishes of Indian Ocean

In this study, oil degrading bacteria discovered from fish living near the oil ports at Karachi in Pakistan were characterized. The bacteria isolated from skin, gills, and gut in fish could consume crude oil as a source of carbon and energy. Total 36 isolates were tested using Nutrient Agar (NA) and MSA media with different crude oil concentrations (0.2%, 0.5%, 0.7%, 1%, 2%, and 5%) and 4 out of 36 isolates (two Gram positive and two Gram negative bacteria) were selected for further identification. 16S rRNA gene sequencing revealed that the isolates are related to Bacillus velezensis, Bacillus flexus, Pseudomonas brenneri and Pseudomonas azotoforman. Oil degrading potential of these bacteria was characterized by GC-MS analysis of degradation of oil components in crude oil as well as engine oil. We found that one (2, 6, 10, 14-Tetramethylpentadecane) out of 42 components in the crude oil was fully eliminated and the other oil components were reduced. In addition, 26 out of 42 oil components in the engine oil, were fully eliminated and the rest were amended. Taken together, these studies identify that B. velezensis, B. flexus, P. brenneri and P. azotoforman have high oil degrading potential, which may be useful for degradation of oil pollutants and other commercial applications.

Artificial Neural Network Models for Large-Scale Data

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.