Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable ...growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.
The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection ...of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.
Leaf shape is spectacularly diverse. As a major component of plant architecture and an interface for light capture, gas exchange, and thermoregulation, the potential contributions of leaves to plant ...fitness are innumerable. Particularly because of their intimate association and interaction with the surrounding environment, both the plasticity of leaf shape during the lifetime of a plant and the evolution of leaf shape over geologic time are revealing with respect to leaf function. Leaf shapes arise within a developmental context that constrains both their evolution and environmental plasticity. Quantitative models capturing genetic diversity, developmental context, and environmental plasticity will be required to fully understand the evolution and development of leaf shape and its response to environmental pressures. In this review, we discuss recent literature demonstrating that distinct molecular pathways are modulated by specific environmental inputs, the output of which regulates leaf dissection. We propose a synthesis explaining both historical patterns in the paleorecord and conserved plastic responses in extant plants. Understanding the potential adaptive value of leaf shape, and how to molecularly manipulate it, will prove to be invaluable in designing crops optimized for future climates.
Leaf shape is spectacularly diverse. Here, Chitwood and Sinha discuss recent literature demonstrating that distinct molecular pathways are modulated by specific environmental inputs, the output of which regulates leaf dissection. A synthesis explaining both historical patterns in the paleorecord and conserved plastic responses in extant plants is also proposed.
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We study operator complexity on various time scales with emphasis on those much larger than the scrambling period. We use, for systems with a large but finite number of degrees of freedom, ...the notion of K-complexity employed in
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for infinite systems. We present evidence that K-complexity of ETH operators has indeed the character associated with the bulk time evolution of extremal volumes and actions. Namely, after a period of exponential growth during the scrambling period the K-complexity increases only linearly with time for exponentially long times in terms of the entropy, and it eventually saturates at a constant value also exponential in terms of the entropy. This constant value depends on the Hamiltonian and the operator but not on any extrinsic tolerance parameter. Thus K-complexity deserves to be an entry in the AdS/CFT dictionary. Invoking a concept of K-entropy and some numerical examples we also discuss the extent to which the long period of linear complexity growth entails an efficient randomization of operators.
Modern sensors find their wide usage in a variety of applications such as robotics, navigation, automation, remote sensing, underwater imaging, etc. and in recent years the sensors with advanced ...techniques such as the artificial intelligence (AI) play a significant role in the field of remote sensing and smart agriculture. The AI enabled sensors work as smart sensors and additionally the advent of the Internet of Things (IoT) has resulted into very useful tools in the field of agriculture by making available different types of sensor-based equipment and devices. In this paper, we have focused on an extensive study of the advances in smart sensors and IoT, employed in remote sensing and agriculture applications such as the assessment of weather conditions and soil quality; the crop monitoring; the use of robots for harvesting and weeding; the employment of drones. The emphasis has been given to specific types of sensors and sensor technologies by presenting an extensive study, review, comparison and recommendation for advancements in IoT that would help researchers, agriculturists, remote sensing scientists and policy makers in their research and implementations.
WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR ...update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.
Previously we found that inhibitor of differentiation 3 (Id3) gene, a transcriptional repressor, efficiently inhibits corneal keratocyte differentiation to myofibroblasts in vitro. This study ...evaluated the potential of adeno-associated virus 5 (AAV5)-mediated Id3 gene therapy to treat corneal scarring using an established rabbit in vivo disease model. Corneal scarring/fibrosis in rabbit eyes was induced by alkali trauma, and 24 h thereafter corneas were administered with either balanced salt solution AAV5-naked vector, or AAV5-Id3 vector (n = 6/group) via an optimized reported method. Therapeutic effects of AAV5-Id3 gene therapy on corneal pathology and ocular health were evaluated with clinical, histological, and molecular techniques. Localized AAV5-Id3 gene therapy significantly inhibited corneal fibrosis/haze clinically from 2.7 to 0.7 on the Fantes scale in live animals (AAV5-naked versus AAV5-Id3; p < 0.001). Furthermore, AAV5-Id3 treatment significantly reduced profibrotic gene mRNA levels: α-smooth muscle actin (α-SMA) (2.8-fold; p < 0.001), fibronectin (3.2-fold; p < 0.001), collagen I (0.8-fold; p < 0.001), and collagen III (1.4-fold; p < 0.001), as well as protein levels of α-SMA (23.8%; p < 0.001) and collagens (1.8-fold; p < 0.001). The anti-fibrotic activity of AAV5-Id3 is attributed to reduced myofibroblast formation by disrupting the binding of E-box proteins to the promoter of α-SMA, a transforming growth factor-β signaling downstream target gene. In conclusion, these results indicate that localized AAV5-Id3 delivery in stroma caused no clinically relevant ocular symptoms or corneal cellular toxicity in the rabbit eyes.
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Our results indicate that localized AAV5-Id3 delivery in stroma caused no clinically relevant ocular symptoms or corneal cellular toxicity in rabbit eyes.
In a very short time, since their emergence, the field of controlled delivery of proteins has grown immensely. Because of their relatively large size, they have low transdermal bioavailabilities. ...Oral bioavailability is generally poor since they are poorly absorbed and easily degraded by proteolytic enzymes in the gastrointestinal tract. Ocular and nasal delivery is also unfavorable due to degradation by enzymes present in eye tissues and nasal mucosa. Thus parenteral delivery is currently most demanding and suitable for delivery of such molecules. In systemic delivery of proteins, biodegradable microspheres as parenteral depot formulation occupy an important place because of several aspects like protection of sensitive proteins from degradation, prolonged or modified release, pulsatile release patterns. The main objective in developing controlled release protein injectables is avoidance of regular invasive doses which in turn provide patient compliance, comfort as well as control over blood levels. This review presents the outstanding contributions in field of biodegradable microspheres as protein delivery systems, their methods of preparation, drug release, stability, interaction with immune system and regulatory considerations.
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