Development of a metagenomic DNA extraction procedure and PCR detection of human enteric bacteria in vegetable salad tissues

Outbreaks of illness due to human enteric pathogenic bacteria via fresh vegetables warrant intensive research on changing strategies of these bacteria in alterning their hosts for survival. The systemic infection of human pathogenic bacteria in plants and the plant growth stage at which they establish endophytic relationship is poorly understood. Since cucumber and carrot are major vegetables consumed in the form of unprocessed salads in India, our study aimed at determination of infection abilities of Salmonella enterica sub sp. enterica and Aeromonas hydrophila in carrot and cucumber, respectively based on a  metagenomic detection system. We report an optimized metagenomic DNA isolation procedure from vegetable tissues co-cultivated with bacteria under laboratory conditions. Colonization of bacteria in vegetable tissues was studied by amplification of bacterial 16S rRNA coding region from the metagenome. DNA obtained from carrot vegetable pieces inoculated with Salmonella resulted in expected amplification of 1.2 kb region of bacterial 16S rRNA source sequences. However, the approach failed to detect Aeromonas in cucumber tissues.  We conclude that carrot could be a symptomless alternate host for  Salmonella sp

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

Adenosine and lymphocyte regulation

Adenosine is a potent extracellular messenger that is produced in high concentrations under metabolically unfavourable conditions. Tissue hypoxia, consequent to a compromised cellular energy status, is followed by the enhanced breakdown of ATP leading to the release of adenosine. Through the interaction with A2 and A3 membrane receptors, adenosine is devoted to the restoration of tissue homeostasis, acting as a retaliatory metabolite. Several aspects of the immune response have to be taken into consideration and even though in general it is very important to dampen inflammation, in some circumstances, such as the case of cancer, it is also necessary to increase the activity of immune cells against pathogens. Therefore, adenosine receptors that are defined as ‘sensors–of metabolic changes in the local tissue environment may be very important targets for modulation of immune responses and drugs devoted to regulating the adenosinergic system are promising in different clinical situations

Telomerase activity as an adjunct to high-risk human papillomavirus types 16 and 18 and cytology screening in cervical cancer

Telomerase is a ribonucleoprotein comprising an RNA template, the telomerase-associated protein and its catalytic subunit, human telomerase reverse transcriptase (hTERT). Telomerase activation is a critical step in cellular immortalisation and development of cancer. Enhanced telomerase activity has been demonstrated in cervical cancer. In the present study telomerase activity and hTERT mRNA expression were evaluated and correlated with the presence of human papillomavirus (HPV) infection and cytological changes in the cervical lesions. Telomerase activity was assayed by telomeric repeat amplification protocol, hTERT mRNA expression by reverse transcriptase polymerase chain reaction and presence of high risk HPV (HR-HPV) infection by polymerase chain reaction. Out of 154 cervical samples of different cytology, 90 (58.44%) were positive for HR-HPV types 16/18, while among 55 normal cervical scrapes, 10 (18.18%) were HPV DNA positive. All 59 invasive cancer samples showed a very high telomerase activity. Among dysplasia, seven (63.6%) mild dysplasia, 18 (100%) of moderate, 20 (100%) of severe dysplasia and 6 (100%) carcinoma in situ (CIS) samples were positive with mild to moderate to high to very high telomerase activity respectively. Seven (12.7%) samples of apparently normal cervical scrapes were weakly positive for telomerase activity. We observed a good correlation (P<0.001) between telomerase activity and HR-HPV 16/18 positivity with a sensitivity of 88.1% for HPV and 100% for telomerase activity. It is suggested that telomerase activity may be used as an adjunct to cytology and HPV DNA testing in triaging women with cervical lesions

Root Depth Prediction Using Machine Learning for Effective Root Zone Injection Irrigation through IoT Automation

Agriculture plays an important role in producing food supply for survival. The agriculture practice is performed by multiple factors such as soil type, fertilisers, plant samplings, irrigation practice, etc. On these factors on which agriculture depends, irrigation is one of the major factors for the better yield of crops, just like any other factor. Therefore, efficient irrigation practice has to be performed to increase cultivation production and preserve available water resources for optimal usage. Traditional methods of irrigation practice are well suited for situations with surplus water resources but not very efficient when it comes to scarce places. So, we propose a new IoT-driven root zone injection method of irrigation which is estimated to perform required irrigation practices in places of high water scarcity. This method is performed with the help of machine learning, IoT devices, wireless neural networking of sensors, and root zone injection equipment for automation. The mechanism starts with collecting real-time data from the agricultural field for specified crop types by using a wireless neural network of sensors and forming the dataset. Once the dataset is formed, it will be processed and cleaned to feed into machine learning algorithms. The machine learning algorithm (here, it is linear regression) will make the required prediction for the water content needed for the irrigation process for that particular day. The dynamic estimation is made as the water content required will vary from the growing phases of plants where it is minimum at the initial phase, peak at middle and reduce or increase depending on the plant species at later phase of growth. This estimated water content is then delivered to the plants through the irrigation process, governed by the IoT devices, which have the procedures encoded for irrigation. ML prediction guides the IoT system on how much water to deliver to the plants. Finally, the injection setup of the root zone passes the water directly to the underground root zones. Thus, completely preventing evaporation wastage and accurate water content estimation and supply, achieving optimal irrigation practice

Root Depth Prediction Using Machine Learning for Effective Root Zone Injection Irrigation Through IoT Automation

Agriculture plays an important role in producing food supply for survival. The agriculture practice is performed by multiple factors such as soil type, fertilisers, plant samplings, irrigation practice, etc. On these factors on which agriculture depends, irrigation is one of the major factors for the better yield of crops, just like any other factor. Therefore, efficient irrigation practice has to be performed to increase cultivation production and preserve available water resources for optimal usage. Traditional methods of irrigation practice are well suited for situations with surplus water resources but not very efficient when it comes to scarce places. So, we propose a new IoT-driven root zone injection method of irrigation which is estimated to perform required irrigation practices in places of high water scarcity. This method is performed with the help of machine learning, IoT devices, wireless neural networking of sensors, and root zone injection equipment for automation. The mechanism starts with collecting real-time data from the agricultural field for specified crop types by using a wireless neural network of sensors and forming the dataset. Once the dataset is formed, it will be processed and cleaned to feed into machine learning algorithms. The machine learning algorithm (here, it is linear regression) will make the required prediction for the water content needed for the irrigation process for that particular day. The dynamic estimation is made as the water content required will vary from the growing phases of plants where it is minimum at the initial phase, peak at middle and reduce or increase depending on the plant species at later phase of growth. This estimated water content is then delivered to the plants through the irrigation process, governed by the IoT devices, which have the procedures encoded for irrigation. ML prediction guides the IoT system on how much water to deliver to the plants. Finally, the injection setup of the root zone passes the water directly to the underground root zones. Thus, completely preventing evaporation wastage and accurate water content estimation and supply, achieving optimal irrigation practice

Novel approach for the bulk synthesis of nanocrystalline yttria doped thoria powders via polymeric precursor routes

Three different polymeric precursor routes viz., (i) amorphous citrate process, (ii) Pechini process and (iii) polyethylene glycol assisted process were compared for synthesising nanocrystalline powders of 7.5 mol% yttria doped thoria (YDT). In each of the processes, parameters such as metal-to-fuel ratio or composition of fuel were varied and the effects were analysed. TG/DTA studies were conducted to identify the ignition temperatures of the precursors. Also, a novel experimental procedure with controlled combustion was devised for the preparation of powders based on the thermal analysis data. All the processes result in phase pure and nanocrystalline powders. The average crystallite size of the powders ranged between 9 and 18 nm. The powder samples were analysed for their carbon content and studied for their sinterability. Densities as high as 99% th.d. could be achieved by sintering the compacts of powders obtained from (i) amorphous citrate process with CA/M ratios 2.0 and 3.0, (ii) Pechini processes (independent of fuel composition) and (iii) PEG-assisted process using PEG 4000 at a relatively low temperature of 1500 degreesC for 2 h. (C) 200