North Eastern Hill University

Keeping in view, the challenges concerning agro-ecosystem and environment, the recent developments in biotechnology offers a more reliable approach to address the food security for future generations and also resolve the complex environmental problems. Several unique features of cyanobacteria such as oxygenic photosynthesis, high biomass yield, growth on non-arable lands and a wide variety of water sources (contaminated and polluted waters), generation of useful by-products and bio-fuels, enhancing the soil fertility and reducing green house gas emissions, have collectively offered these bio-agents as the precious bio-resource for sustainable development. Cyanobacterial biomass is the effective bio-fertilizer source to improve soil physico-chemical characteristics such as water-holding capacity and mineral nutrient status of the degraded lands. The unique characteristics of cyanobacteria include their ubiquity presence, short generation time and capability to fix the atmospheric N2. Similar to other prokaryotic bacteria, the cyanobacteria are increasingly applied as bio-inoculants for improving soil fertility and environmental quality. Genetically engineered cyanobacteria have been devised with the novel genes for the production of a number of bio-fuels such as bio-diesel, bio-hydrogen, bio-methane, synga, and therefore, open new avenues for the generation of bio-fuels in the economically sustainable manner. This review is an effort to enlist the valuable information about the qualities of cyanobacteria and their potential role in solving the agricultural and environmental problems for the future welfare of the planet.

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

Many, though not all, non-heterocystous cyanobacteria can fix N-2. However, very few strains can fix N-2 aerobically. Nevertheless, these organisms may make a substantial contribution to the global nitrogen cycle. In this general review, N-2 fixation by laboratory cultures and natural populations of non-heterocystous cyanobacteria is considered. The properties and subcellular location of nitrogenase in these organisms is described, as is the response of N-2 fixation to environmental factors such as fixed nitrogen, O-2 and the pattern of illumination. The integration of N-2 fixation with other aspects of cell metabolism (in particular photosynthesis) is also discussed. Similarities and differences between different individual strains of nonheterocystous cyanobacteria are highlighted. [KEYWORDS: non-heterocystous cyanobacteria; N-2 fixation; nitrogenase; immunolocalization; ATP and reductant; diurnal rhythm; natural environment Aerobic nitrogen-fixation; filamentous nonheterocystous cyanobacterium; synechococcus sp rf-1; marine microbial mat; anabaena-variabilis atcc-29413; plectonema-boryanum pcc-73110; circadian gene-expression; sp strain pcc-6803; sp pcc 6909; unicellular cyanobacterium]

Autophagy causes the breakdown of damaged proteins and organelles to their constituent components. The phosphatidylinositol 3-kinase (PI3K) pathway played an important role in regulating the autophagic response of cells in response to changing reactive oxygen species (ROS) levels. The PI3K α catalytic subunit inhibits autophagy, while its β catalytic subunit promotes autophagy in response to changes in ROS levels. The downstream Akt protein acts against autophagy initiation in response to increases in ROS levels under nutrient-rich conditions. Akt acts by activating a mechanistic target of the rapamycin complex 1 (mTORC1) and by arresting autophagic gene expression. The AMP-activated protein kinase (AMPK) protein counteracts the Akt actions. mTORC1 and mTORC2 inhibit autophagy under moderate ROS levels, but under high ROS levels, mTORC2 can promote cellular senescence via autophagy. Phosphatase and tensin homolog (PTEN) protein are the negative regulators of the PI3K pathway, and it has proautophagic activities. Studies conducted on cells treated with flavonoids and ionizing radiation showed that the moderate increase in ROS levels in the flavonoid-treated groups corresponded with higher PTEN levels and lowered Akt levels leading to a higher occurrence of autophagy. In contrast, higher ROS levels evoked by ionizing radiation caused a lowering of the incidence of autophagy.

Cyanobacteria are an ancient, morphologically diverse group of prokaryotes with an oxygenic photosynthesis. Many cyanobacteria also possess the ability to fix N 2 . Although well suited to an independent existence in nature, some cyanobacteria occur in symbiosis with a wide range of hosts (protists, animals and plants). Among plants, such symbioses have independently evolved in phylogenetically diverse genera belonging to the algae, fungi, bryophytes, pteridophytes, gymnosperms and angiosperms. These are N 2 ‐fixing symbioses involving heterocystous cyanobacteria, particularly Nostoc , as cyanobionts (cyanobacterial partners). A given host species associates with only a particular cyanobiont genus but such specificity does not extend to the strain level. The cyanobiont is located under a microaerobic environment in a variety of host organs and tissues (bladder, thalli and cephalodia in fungi; cavities in gametophytes of hornworts and liverworts or fronds of the Azolla sporophyte; coralloid roots in cycads; stem glands in Gunnera ). Except for fungi, the hosts form these structures ahead of the cyanobiont infection. The symbiosis lasts for one generation except in Azolla and diatoms, in which it is perpetuated from generation to generation. Within each generation, multiple fresh infections occur as new symbiotic tissues and organs develop. The symbioses are stable over a wide range of environmental conditions, and sensing–signalling between partners ensures their synchronized growth and development. The cyanobiont population is kept constant in relation to the host biomass through controlled initiation and infection, nutrient supply and cell division. In most cases, the partners have remained facultative, with the cyanobiont residing extracellularly in the host. However, in the water‐fern Azolla and the freshwater diatom Rhopalodia the association is obligate. The cyanobionts occur intracellularly in diatoms, the fungus Geosiphon and the angiosperm Gunner a . Close cell–cell contact and the development of special structures ensure efficient nutrient exchange between the partners. The mobile nutrients are normal products of the donor cells, although their production is increased in symbiosis. Establishment of cyanobacterial–plant symbioses differs from chloroplast evolution. In these symbioses, the cyanobiont undergoes structural–functional changes suited to its role as provider of fixed N rather than fixed C, and the level of intimacy is far less than that of an organelle. This review provides an updated account of cyanobacterial–plant symbioses, particularly concerning developments during the past 10 yr. Various aspects of these symbioses such as initiation and development, symbiont diversity, recognition and signalling, structural–functional modifications, integration, and nutrient exchange are reviewed and discussed, as are evolutionary aspects and the potential uses of cyanobacterial–plant symbioses. Finally we outline areas that require special attention for future research. Not only will these provide information of academic interest but they will also help to improve the use of Azolla as green manure, to enable us to establish artificial N 2 ‐fixing associations with cereals such as rice, and to allow the manipulation of free‐living cyanobacteria for photobiological ammonia or hydrogen production or for use as biofertilizers. contents Summary 449 I. introduction 450 II. the partners 451 III. initiation and development of symbioses 458 IV. the symbioses 462 V. evolutionary aspects 472 VI. artificial symbioses 474 VII. future outlook and perspectives 475 Acknowledgements 477 References 477

The members of the actinomycete genus Frankia are nitrogen-fixing symbionts of may species of woody dicotyledonous plants belonging to eight families. Several strains isolated from diverse actinorhizal plants growing in different geographical areas were used in this study. The phylogenetic relationships of these organisms and uncharacterized microsymbionts that are recalcitrant to isolation in pure culture were determined by comparing complete 16S ribosomal DNA sequences. The resulting phylogenetic tree revealed that there was greater diversity among the Alnus-infective strains than among the strains that infect other host plants. The four main subdivisions of the genus Frankia revealed by this phylogenetic analysis are (i) a very large group comprising Frankia alni and related organisms (including Alnus rugosa Sp+ microsymbionts that are seldom isolated in pure culture), to which Casuarina-infective strains, a Myrica nagi microsymbiont, and other effective Alnus-infective strains are related; (ii) unisolated microsymbionts of Dryas, Coriaria, and Datisca species; (iii) Elaeagnus-infective strains; and (iv) "atypical" strains (a group which includes an Alnus-infective, non-nitrogen-fixing strain). Taxa that are related to this well-defined, coherent Frankia cluster are the genera Geodermatophilus, "Blastococcus," Sporichthya, Acidothermus, and Actinoplanes. However, the two genera whose members have multilocular sporangia (the genera Frankia and Geodermatophilus) did not form a coherent group. For this reason, we propose that the family Frankiaceae should be emended so that the genera Geodermatophilus and "Blastococcus" are excluded and only the genus Frankia is retained.

Abstract: Bamboo is intricately associated with humans from times immemorial. Popularly known for their industrial uses, a lesser known fact of bamboos is the usage of its young shoots as a food that can be consumed fresh, fermented, or canned. The juvenile shoots are not only delicious but are rich in nutrient components, mainly proteins, carbohydrates, minerals, and fiber and are low in fat and sugars. In addition, they contain phytosterols and a high amount of fiber that can be labeled as nutraceuticals or natural medicines that are attracting the attention of health advocates and scientists alike. The shoots are free from residual toxicity and grow without the application of fertilizers. Modern research has revealed that bamboo shoots have a number of health benefits: improving appetite and digestion, weight loss, and curing cardiovascular diseases and cancer. The shoots are reported to have anticancer, antibacterial, and antiviral activity. Shoots have antioxidant capacity due to the presence of phenolic compounds. The increasing trends of health consciousness among consumers have stimulated the field of functional foods and bamboo shoots can be one of them. Bamboo fiber is now a common ingredient in breakfast cereals, fruit juices, bakery and meat products, sauces, shredded cheeses, cookies, pastas, snacks, frozen desserts, and many other food products. This review emphasizes the health benefits of bamboo shoots and their potential for utilization as a health food.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

The present work studies one of Einstein's alternative formulations based on the nonmetricity scalar $Q$ generalized as $f(Q)$ theory. More specifically, we consider the power-law form of $f(Q)$ gravity, i.e., $f(Q)=Q+\ensuremath{\alpha}{Q}^{n}$. Here, we analyze the behavior of the cosmological model at the background and perturbation level. Using the dynamical system analysis, at the background level, we find the effective evolution of the model is the same as that of the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ for $|n|<1$. Interestingly, the geometric component of the theory solely determined the late-time acceleration of the Universe. We also examine the integrability of the model by employing the method of singularity analysis. In particular, we find the conditions under which field equations pass the Painlev\'e test and hence possess the Painlev\'e property. While the equations pass the Painlev\'e test in the presence of dust for any value of $n$, the test is valid after the addition of radiation fluid only for $n<1$. Finally, at the perturbation level, the behavior of matter growth index signifies a deviation of the model from the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ even for $|n|<1$.

‘Give a man a biosensor, and you enable him to unlock a world of cost-effective solutions for research, diagnosis, and personalized healthcare’. Biosensors have emerged as a game-changer in the realms of research sciences and healthcare, offering exceptional value for money. The integration of biosensors into these fields holds immense promise, empowering researchers and medical practitioners to unlock intricate mysteries in food and water safety, human biology, and health assessment. These state-of-the-art technologies are a breath of fresh air, revolutionizing disease detection and tracking to unprecedented levels, elevating the ability to monitor the body's response. They have become the linchpin of numerous cost-effective, highly efficient, and streamlined medical devices prevalent in modern healthcare. By harnessing the sensitivity and specificity of biosensors, healthcare professionals can hit the nail on the head, identifying even the subtlest biomarkers and indicators of various ailments, and enabling timely intervention and treatment. The superior quality of these biosensors ensures unrivaled diagnostic accuracy, leading to more reliable and effective healthcare outcomes. In a nutshell, biosensors have raised the bar, making research, public safety, and tailored healthcare options a walk in the park, ultimately enhancing overall health and well-being. Biosensors offer immense potential in medical diagnostics due to their user-friendly nature, scalability, and efficient manufacturing. With intelligent wearable features, they facilitate seamless health monitoring for the elderly, bridging the gap between self-care and healthcare providers. This exchange of medical information reduces interference and hospital visits, opening avenues in wellness, fitness, and athletics for consumers and commercial entities. This paper explores the advancements in Biosensors technology and their promising benefits in medicine, focusing on cardiovascular diseases and using informative diagrams. It examines fourteen key applications of Biosensors in the medical field, highlighting the integration of biomedical devices, apps, firmware, and advanced algorithms. These developments pave the way for innovative medical therapies, real-time evidence-based insights, customized solutions, and informed guidance, shaping a bright future for healthcare.

The timely identification of plant diseases prevents the negative impact on crops. Convolutional neural network, particularly deep learning is used widely in machine vision and pattern recognition task. Researchers proposed different deep learning models in the identification of diseases in plants. However, the deep learning models require a large number of parameters, and hence the required training time is more and also difficult to implement on small devices. In this paper, we have proposed a novel deep learning model based on the inception layer and residual connection. Depthwise separable convolution is used to reduce the number of parameters. The proposed model has been trained and tested on three different plant diseases datasets. The performance accuracy obtained on plantvillage dataset is 99.39%, on the rice disease dataset is 99.66%, and on the cassava dataset is 76.59%. With fewer number of parameters, the proposed model achieves higher accuracy in comparison with the state-of-art deep learning models.

This narrative review summarizes the latest advances in cerebral palsy and identifies where more research is required. Several studies on cerebral palsy were analyzed to generate a general idea of the prevalence of, risk factors associated with, and classification of cerebral palsy (CP). Different classification systems used for the classification of CP on a functional basis were also analyzed. Diagnosis systems used along with the prevention techniques were discussed. State-of-the-art treatment strategies for CP were also analyzed. Statistical distribution was performed based on the selected studies. Prevalence was found to be 2-3/1000 lives; the factors that can be correlated are gestational age and birth weight. The risk factors identified were preconception, prenatal, perinatal, and postnatal categories. According to the evidence, CP is classified into spastic (80%), dyskinetic (15%), and ataxic (5%) forms. Diagnosis approaches were based on clinical investigation and neurological examinations that include magnetic resonance imaging (MRI), biomarkers, and cranial ultrasound. The treatment procedures found were medical and surgical interventions, physiotherapy, occupational therapy, umbilical milking, nanomedicine, and stem cell therapy. Technological advancements in CP were also discussed. CP is the most common neuromotor disability with a prevalence of 2-3/1000 lives. The highest contributing risk factor is prematurity and being underweight. Several preventions and diagnostic techniques like MRI and ultrasound were being used. Treatment like cord blood treatment nanomedicine and stem cell therapy needs to be investigated further in the future to apply in clinical practice. Future studies are indicated in the context of technological advancements among cerebral palsy children.

The phytoremediation is an environment friendly, green technology that is cost effective and energetically inexpensive. Metal hyperaccumulator plants are used to remove metal from terrestrial as well as aquatic ecosystems. The technique makes use of the intrinsic capacity of plants to accumulate metal and transport them to shoots, ability to form phytochelatins in roots and sequester the metal ions. Harbouring the genes that are considered as signatures for the tolerance and hyperaccumulation from identified hyperaccumulator plant species into the transgenic plants provide a platform to develop the technology with the help of genetic engineering. This would result in transgenics that may have large biomass and fast growth a quality essential for removal of metal from soil quickly and in large quantities. Despite so much of a potential, the progress in the field of developing transgenic phytoremediator plant species is rather slow. This can be attributed to the lack of our understanding of complex interactions in the soil and indigenous mechanisms in the plants that allow metal translocation, accumulation and removal from a site. The review focuses on the work carried out in the field of metal phytoremediation from contaminated soil. The paper concludes with an assessment of the current status of technology development and its future prospects with emphasis on a combinatorial approach.

PFR8 which produced average particle sizes of 8.7 ±6 nm and 7.7 ±4.3 nm, respectively. The energy dispersive X-ray spectroscopy (EDS) technique in conjunction with scanning electron microscopy was used for the elemental analysis of the nanoparticles. The selected area diffraction pattern recorded from single particle in the aggregates of nanoparticles revealed that the silver particles are crystalline in nature.

Betel nut (BN), betel quid (BQ) and products derived from them are widely used as a socially endorsed masticatory product. The addictive nature of BN/BQ has resulted in its widespread usage making it the fourth most abused substance by humans. Progressively, several additives, including chewing tobacco, got added to simple BN preparations. This addictive practice has been shown to have strong etiological correlation with human susceptibility to cancer, particularly oral and oropharyngeal cancers.The PUBMED database was searched to retrieve all relevant published studies in English on BN and BQ, and its association with oral and oropharyngeal cancers. Only complete studies directly dealing with BN/BQ induced carcinogenesis using statistically valid and acceptable sample size were analyzed. Additional relevant information available from other sources was also considered.This systematic review attempts to put in perspective the consequences of this widespread habit of BN/BQ mastication, practiced by approximately 10% of the world population, on oral cancer with a clinical perspective. BN/BQ mastication seems to be significantly associated with susceptibility to oral and oropharyngeal cancers. Addition of tobacco to BN has been found to only marginally increase the cancer risk. Despite the widespread usage of BN/BQ and its strong association with human susceptibility to cancer, no serious strategy seems to exist to control this habit. The review, therefore, also looks at various preventive efforts being made by governments and highlights the multifaceted intervention strategies required to mitigate and/or control the habit of BN/BQ mastication.

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

1,3-Dicarbonyl compounds can be converted to Mannich-type products A or highly functionalized piperidines B in the presence of a catalytic amount of bromodimethylsulfonium bromide (BDMS). The combination of aromatic aldehyde, amine, and 1,3-dicarbonyl compounds in the presence of a catalytic amount of BDMS leads to the formation of Mannich-type product A when R is a non-enolizable carbon or an alkoxy group, whereas in cases when R = CH3, the same combination yielded highly functionalized piperidines B. A synthetic study and mechanistic proposal are presented.

This survey of metal–metal (MM) bond distances in binuclear complexes of the first row 3d-block elements reviews experimental and computational research on a wide range of such systems. The metals surveyed are titanium, vanadium, chromium, manganese, iron, cobalt, nickel, copper, and zinc, representing the only comprehensive presentation of such results to date. Factors impacting MM bond lengths that are discussed here include (a) the formal MM bond order, (b) size of the metal ion present in the bimetallic core (M2)n+, (c) the metal oxidation state, (d) effects of ligand basicity, coordination mode and number, and (e) steric effects of bulky ligands. Correlations between experimental and computational findings are examined wherever possible, often yielding good agreement for MM bond lengths. The formal bond order provides a key basis for assessing experimental and computationally derived MM bond lengths. The effects of change in the metal upon MM bond length ranges in binuclear complexes suggest trends for single, double, triple, and quadruple MM bonds which are related to the available information on metal atomic radii. It emerges that while specific factors for a limited range of complexes are found to have their expected impact in many cases, the assessment of the net effect of these factors is challenging. The combination of experimental and computational results leads us to propose for the first time the ranges and “best” estimates for MM bond distances of all types (Ti–Ti through Zn–Zn, single through quintuple).