KIIT University

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.

Nanotechnology is the creation, manipulation and use of materials at the nanometre size scale (1 to 100 nm). At this size scale there are significant differences in many material properties that are normally not seen in the same materials at larger scales. Although nanoscale materials can be produced using a variety of traditional physical and chemical processes, it is now possible to biologically synthesize materials via environment-friendly green chemistry based techniques. In recent years, the convergence between nanotechnology and biology has created the new field of nanobiotechnology that incorporates the use of biological entities such as actinomycetes algae, bacteria, fungi, viruses, yeasts, and plants in a number of biochemical and biophysical processes. The biological synthesis via nanobiotechnology processes have a significant potential to boost nanoparticles production without the use of harsh, toxic, and expensive chemicals commonly used in conventional physical and chemical processes. The aim of this review is to provide an overview of recent trends in synthesizing nanoparticles via biological entities and their potential applications.

Soils are subject to varying degrees of direct or indirect human disturbance, constituting a major global change driver. Factoring out natural from direct and indirect human influence is not always straightforward, but some human activities have clear impacts. These include land-use change, land management and land degradation (erosion, compaction, sealing and salinization). The intensity of land use also exerts a great impact on soils, and soils are also subject to indirect impacts arising from human activity, such as acid deposition (sulphur and nitrogen) and heavy metal pollution. In this critical review, we report the state-of-the-art understanding of these global change pressures on soils, identify knowledge gaps and research challenges and highlight actions and policies to minimize adverse environmental impacts arising from these global change drivers. Soils are central to considerations of what constitutes sustainable intensification. Therefore, ensuring that vulnerable and high environmental value soils are considered when protecting important habitats and ecosystems, will help to reduce the pressure on land from global change drivers. To ensure that soils are protected as part of wider environmental efforts, a global soil resilience programme should be considered, to monitor, recover or sustain soil fertility and function, and to enhance the ecosystem services provided by soils. Soils cannot, and should not, be considered in isolation of the ecosystems that they underpin and vice versa. The role of soils in supporting ecosystems and natural capital needs greater recognition. The lasting legacy of the International Year of Soils in 2015 should be to put soils at the centre of policy supporting environmental protection and sustainable development.

The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set up in 2004 on acute-on-chronic liver failure (ACLF) was published in 2009. With international groups volunteering to join, the “APASL ACLF Research Consortium (AARC)” was formed in 2012, which continued to collect prospective ACLF patient data. Based on the prospective data analysis of nearly 1400 patients, the AARC consensus was published in 2014. In the past nearly four-and-a-half years, the AARC database has been enriched to about 5200 cases by major hepatology centers across Asia. The data published during the interim period were carefully analyzed and areas of contention and new developments in the field of ACLF were prioritized in a systematic manner. The AARC database was also approached for answering some of the issues where published data were limited, such as liver failure grading, its impact on the ‘Golden Therapeutic Window’, extrahepatic organ dysfunction and failure, development of sepsis, distinctive features of acute decompensation from ACLF and pediatric ACLF and the issues were analyzed. These initiatives concluded in a two-day meeting in October 2018 at New Delhi with finalization of the new AARC consensus. Only those statements, which were based on evidence using the Grade System and were unanimously recommended, were accepted. Finalized statements were again circulated to all the experts and subsequently presented at the AARC investigators meeting at the AASLD in November 2018. The suggestions from the experts were used to revise and finalize the consensus. After detailed deliberations and data analysis, the original definition of ACLF was found to withstand the test of time and be able to identify a homogenous group of patients presenting with liver failure. New management options including the algorithms for the management of coagulation disorders, renal replacement therapy, sepsis, variceal bleed, antivirals and criteria for liver transplantation for ACLF patients were proposed. The final consensus statements along with the relevant background information and areas requiring future studies are presented here.

k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. kNN groups the data into coherent clusters or subsets and classifies the newly inputted data based on its similarity with previously trained data. The input is assigned to the class with which it shares the most nearest neighbors. Though kNN is effective, it has many weaknesses. This paper highlights the kNN method and its modified versions available in previously done researches. These variants remove the weaknesses of kNN and provide a more efficient method.

Copper indium gallium selenide (CIGS) based solar cells are receiving worldwide attention for solar power generation.

A wide range of unregulated chemicals of synthetic origin or derived from natural sources, which may be a contender for future regulations are called Emerging Contaminants (ECs). The concentration of ECs ranges from ng/L to μg/L, which is comparatively smaller as compared to other pollutants present in water and wastewater. Even though the environmental concentration is low, ECs still possess a great threat to the humans and ecosystem. These compounds are being widely studied due to their potential health effects, pervasive nature, and difficult degradation through conventional techniques. Pharmaceutical active compounds (PhACs) or pharmaceutical contaminants (PCs) are one of the major groups of ECs which can cause inimical effect on living organisms even at very lower concentration. These contaminants don't degrade easily and persistent for longer periods in the environment due to their stable structure. With the increase in demand of Pharmaceuticals and Personal Care Products (PPCPs), there has been a sharp increase of these pollutants in water bodies. This is mainly due to the inefficiency of conventional wastewater treatment plants in treatment and removal of these PhACs. The proper identification of pharmaceutical groups and development of removal techniques is crucial in the recent times. This review represents a comprehensive summary on PCs, various groups of PCs and an overview of approaches and treatment systems available for their removal. Efficient and effective treatment methods can be useful for completely eradicating these compounds and making the water bodies safe for use. So, the investment of capital and time on research on PCs and their removal techniques can be beneficial for the future.

Diabetes mellitus (DM) is a metabolic disorder that occurs in the body because of decreased insulin activity and/or insulin secretion. Pathological changes such as nephropathy, retinopathy, and cardiovascular complications inevitably occur in the body with the progression of the disease. DM is mainly categorized into 2 sub-types, type I DM and type II DM. While type I DM is generally treated through insulin replacement therapy, type II DM is treated with oral hypoglycaemics. The major drug therapy for type II DM comprises of insulin secretagogues, biguanides, insulin sensitizers, alpha glucosidase inhibitors, incretin mimetics, amylin antagonists and sodium-glucose co-transporter-2 (SGLT2) inhibitors. Dual drug therapies are often recommended in patients who are unable to achieve therapeutic goals with first line oral hypoglycaemic agents as monotherapy. Inspite of the appreciable therapeutic benefits, the conventional dosage forms depicts differential bioavailability and short half-life, mandating frequent dosage and causing greater side effects leading to therapy ineffectiveness and patient non-compliance. Given the pathological complexity of the said disease, nanotechnology-based approaches are more enticing as it comes with added advantage of site-specific drug delivery with higher bioavailability and reduced dosage regimen. In the present review article, we have made an attempt to explore the pathophysiology of type II DM, the conventional treatment approaches (mono and combination therapy) as well as the nano based drug delivery approaches for the treatment of type II DM.

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

BACKGROUND: A third of the 2·5 billion people worldwide without access to improved sanitation live in India, as do two-thirds of the 1·1 billion practising open defecation and a quarter of the 1·5 million who die annually from diarrhoeal diseases. We aimed to assess the effectiveness of a rural sanitation intervention, within the context of the Government of India's Total Sanitation Campaign, to prevent diarrhoea, soil-transmitted helminth infection, and child malnutrition. METHODS: We did a cluster-randomised controlled trial between May 20, 2010, and Dec 22, 2013, in 100 rural villages in Odisha, India. Households within villages were eligible if they had a child younger than 4 years or a pregnant woman. Villages were randomly assigned (1:1), with a computer-generated sequence, to undergo latrine promotion and construction or to receive no intervention (control). Randomisation was stratified by administrative block to ensure an equal number of intervention and control villages in each block. Masking of participants was not possible because of the nature of the intervention. However, households were not told explicitly that the purpose of enrolment was to study the effect of a trial intervention, and the surveillance team was different from the intervention team. The primary endpoint was 7-day prevalence of reported diarrhoea in children younger than 5 years. We did intention-to-treat and per-protocol analyses. This trial is registered with ClinicalTrials.gov, number NCT01214785. FINDINGS: We randomly assigned 50 villages to the intervention group and 50 villages to the control group. There were 4586 households (24,969 individuals) in intervention villages and 4894 households (25,982 individuals) in control villages. The intervention increased mean village-level latrine coverage from 9% of households to 63%, compared with an increase from 8% to 12% in control villages. Health surveillance data were obtained from 1437 households with children younger than 5 years in the intervention group (1919 children younger than 5 years), and from 1465 households (1916 children younger than 5 years) in the control group. 7-day prevalence of reported diarrhoea in children younger than 5 years was 8·8% in the intervention group and 9·1% in the control group (period prevalence ratio 0·97, 95% CI 0·83-1·12). 162 participants died in the intervention group (11 children younger than 5 years) and 151 died in the control group (13 children younger than 5 years). INTERPRETATION: Increased latrine coverage is generally believed to be effective for reducing exposure to faecal pathogens and preventing disease; however, our results show that this outcome cannot be assumed. As efforts to improve sanitation are being undertaken worldwide, approaches should not only meet international coverage targets, but should also be implemented in a way that achieves uptake, reduces exposure, and delivers genuine health gains. FUNDING: Bill & Melinda Gates Foundation, International Initiative for Impact Evaluation (3ie), and Department for International Development-backed SHARE Research Consortium at the London School of Hygiene & Tropical Medicine.

5-Fluorouracil (5-FU) has been an important anti-cancer drug to date. With an increase in the knowledge of its mechanism of action, various treatment modalities have been developed over the past few decades to increase its anti-cancer activity. But drug resistance has greatly affected the clinical use of 5-FU. Overcoming this chemoresistance is a challenge due to the presence of cancer stem cells like cells, cancer recurrence, metastasis, and angiogenesis. In this review, we have systematically discussed the mechanism of 5-FU resistance and advent strategies to increase the sensitivity of 5-FU therapy including resistance reversal. Special emphasis has been given to the cancer stem cells (CSCs) mediated 5-FU chemoresistance and its reversal process by different approaches including the DNA repair inhibition process.

Due to emergence of new variants of pathogenic micro-organisms the treatment and immunization of infectious diseases have become a great challenge in the past few years. In the context of vaccine development remarkable efforts have been made to develop new vaccines and also to improve the efficacy of existing vaccines against specific diseases. To date, some vaccines are developed from protein subunits or killed pathogens, whilst several vaccines are based on live-attenuated organisms, which carry the risk of regaining their pathogenicity under certain immunocompromised conditions. To avoid this, the development of risk-free effective vaccines in conjunction with adequate delivery systems are considered as an imperative need to obtain desired humoral and cell-mediated immunity against infectious diseases. In the last several years, the use of nanoparticle-based vaccines has received a great attention to improve vaccine efficacy, immunization strategies, and targeted delivery to achieve desired immune responses at the cellular level. To improve vaccine efficacy, these nanocarriers should protect the antigens from premature proteolytic degradation, facilitate antigen uptake and processing by antigen presenting cells, control release, and should be safe for human use. Nanocarriers composed of lipids, proteins, metals or polymers have already been used to attain some of these attributes. In this context, several physico-chemical properties of nanoparticles play an important role in the determination of vaccine efficacy. This review article focuses on the applications of nanocarrier-based vaccine formulations and the strategies used for the functionalization of nanoparticles to accomplish efficient delivery of vaccines in order to induce desired host immunity against infectious diseases.

Neuroprotection is a proactive approach to safeguarding the nervous system, including the brain, spinal cord, and peripheral nerves, by preventing or limiting damage to nerve cells and other components. It primarily defends the central nervous system against injury from acute and progressive neurodegenerative disorders. Oxidative stress, an imbalance between the body's natural defense mechanisms and the generation of reactive oxygen species, is crucial in developing neurological disorders. Due to its high metabolic rate and oxygen consumption, the brain is particularly vulnerable to oxidative stress. Excessive ROS damages the essential biomolecules, leading to cellular malfunction and neurodegeneration. Several neurological disorders, including Alzheimer's, Parkinson's, Amyotrophic lateral sclerosis, multiple sclerosis, and ischemic stroke, are associated with oxidative stress. Understanding the impact of oxidative stress in these conditions is crucial for developing new treatment methods. Researchers are exploring using antioxidants and other molecules to mitigate oxidative stress, aiming to prevent or slow down the progression of brain diseases. By understanding the intricate interplay between oxidative stress and neurological disorders, scientists hope to pave the way for innovative therapeutic and preventive approaches, ultimately improving individuals' living standards.

The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applications, visual recognition systems which encompasses image classification, localization and detection have achieved great research momentum. Due to significant development in neural networks especially deep learning, these visual recognition systems have attained remarkable performance. Object detection is one of these domains witnessing great success in computer vision. This paper demystifies the role of deep learning techniques based on convolutional neural network for object detection. Deep learning frameworks and services available for object detection are also enunciated. Deep learning techniques for state-of-the-art object detection systems are assessed in this paper.

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Abstract. Soils play a pivotal role in major global biogeochemical cycles (carbon, nutrient, and water), while hosting the largest diversity of organisms on land. Because of this, soils deliver fundamental ecosystem services, and management to change a soil process in support of one ecosystem service can either provide co-benefits to other services or result in trade-offs. In this critical review, we report the state-of-the-art understanding concerning the biogeochemical cycles and biodiversity in soil, and relate these to the provisioning, regulating, supporting, and cultural ecosystem services which they underpin. We then outline key knowledge gaps and research challenges, before providing recommendations for management activities to support the continued delivery of ecosystem services from soils. We conclude that, although soils are complex, there are still knowledge gaps, and fundamental research is still needed to better understand the relationships between different facets of soils and the array of ecosystem services they underpin, enough is known to implement best practices now. There is a tendency among soil scientists to dwell on the complexity and knowledge gaps rather than to focus on what we do know and how this knowledge can be put to use to improve the delivery of ecosystem services. A significant challenge is to find effective ways to share knowledge with soil managers and policy makers so that best management can be implemented. A key element of this knowledge exchange must be to raise awareness of the ecosystems services underpinned by soils and thus the natural capital they provide. We know enough to start moving in the right direction while we conduct research to fill in our knowledge gaps. The lasting legacy of the International Year of Soils in 2015 should be for soil scientists to work together with policy makers and land managers to put soils at the centre of environmental policy making and land management decisions.

The intestinal ecosystem is formed by a complex, yet highly characteristic microbial community. The parameters defining whether this community permits invasion of a new bacterial species are unclear. In particular, inhibition of enteropathogen infection by the gut microbiota ( = colonization resistance) is poorly understood. To analyze the mechanisms of microbiota-mediated protection from Salmonella enterica induced enterocolitis, we used a mouse infection model and large scale high-throughput pyrosequencing. In contrast to conventional mice (CON), mice with a gut microbiota of low complexity (LCM) were highly susceptible to S. enterica induced colonization and enterocolitis. Colonization resistance was partially restored in LCM-animals by co-housing with conventional mice for 21 days (LCM(con21)). 16S rRNA sequence analysis comparing LCM, LCM(con21) and CON gut microbiota revealed that gut microbiota complexity increased upon conventionalization and correlated with increased resistance to S. enterica infection. Comparative microbiota analysis of mice with varying degrees of colonization resistance allowed us to identify intestinal ecosystem characteristics associated with susceptibility to S. enterica infection. Moreover, this system enabled us to gain further insights into the general principles of gut ecosystem invasion by non-pathogenic, commensal bacteria. Mice harboring high commensal E. coli densities were more susceptible to S. enterica induced gut inflammation. Similarly, mice with high titers of Lactobacilli were more efficiently colonized by a commensal Lactobacillus reuteri(RR) strain after oral inoculation. Upon examination of 16S rRNA sequence data from 9 CON mice we found that closely related phylotypes generally display significantly correlated abundances (co-occurrence), more so than distantly related phylotypes. Thus, in essence, the presence of closely related species can increase the chance of invasion of newly incoming species into the gut ecosystem. We provide evidence that this principle might be of general validity for invasion of bacteria in preformed gut ecosystems. This might be of relevance for human enteropathogen infections as well as therapeutic use of probiotic commensal bacteria.

Before Convolutional Neural Networks gained popularity, computer recognition problems involved extracting features out of the data provided which was not adequately efficient or provided a high degree of accuracy. However in recent times, Convolutional Neural Networks have attempted to provide a higher level of efficiency and accuracy in all the fields in which it has been employed in most popular of which are Object Detection, Digit and Image Recognition. It employs a definitely algorithm of steps to follow including methods like Backpropagation, Convolutional Layers, Feature formation and Pooling. Also this article will also venture into use of various frameworks and tools that involve CNN model.

Cytokines regulate immune responses essential for maintaining immune homeostasis, as deregulated cytokine signaling can lead to detrimental outcomes, including inflammatory disorders. The antioxidants emerge as promising therapeutic agents because they mitigate oxidative stress and modulate inflammatory pathways. Antioxidants can potentially ameliorate inflammation-related disorders by counteracting excessive cytokine-mediated inflammatory responses. A comprehensive understanding of cytokine-mediated inflammatory pathways and the interplay with antioxidants is paramount for developing natural therapeutic agents targeting inflammation-related disorders and helping to improve clinical outcomes and enhance the quality of life for patients. Among these antioxidants, curcumin, vitamin C, vitamin D, propolis, allicin, and cinnamaldehyde have garnered attention for their anti-inflammatory properties and potential therapeutic benefits. This review highlights the interrelationship between cytokines-mediated disorders in various diseases and therapeutic approaches involving antioxidants. • Cytokines, produced by immune cells, are pivotal players in many health conditions. • The imbalance of cytokines results in an excessive immune response. • Antioxidants provide therapeutic benefits in inflammatory conditions. • Antioxidants combined with radiotherapy can be an effective therapeutic option.