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Mycobacterium avium subspecies paratuberculosis (MAP) infection leads to chronic, persistent granulomatous enteritis, causing prolonged diarrhoea and emaciation. The disease is managed using medications such as antibiotics, live vaccines, mycobacteriophage therapies and other treatments; however, a notable proportion of affected animals do not show improvement with this approach. We hypothesise that immunoinhibitory receptors TIM-3 (T cell immunoglobulin mucin protein-3) and PD-1 (Programmed death receptor 1) may be upregulated on Peripheral blood mononuclear cells (PBMCs) of MAP-seropositive bovines, potentially contributing to immune exhaustion. Samples (blood and faeces) were collected from 32 diarrhoeic bovines suspected of MAP infection; eight apparently healthy buffaloes from the dairy farm at Hisar, Haryana and from 14 cows (suffering from chronic diarrhoea, weakness and emaciation) housed in stray cattle shed. MAP infection was estimated using indigenous ELISA (i-ELISA), faecal IS900 PCR, culture and acid-fast staining. TIM-3 and PD-1 gene expression on PBMCs were determined using qRT-PCR. TIM3 expression was relatively higher (~400-fold, 330-fold, 112-fold, 65-fold and 16-fold) in 5 chronically diarrhoeic PBMCs samples (MAP-seropositive), and higher PD-1 expression (around ~7-fold, 1.75-fold, 2.5-fold, 7.6-fold) was recorded in 4 diarrhoeic MAP-seropositive animals, compared to apparently healthy and other MAP-seronegative diarrhoeic animals. High co-expression of TIM-3 and PD-1 levels was also recorded in chronically diarrhoeic, emaciated stray cattle. Understanding immune responses in field conditions might aid in the therapeutic management of paratuberculosis.

Making a correct diagnosis is the first, and most important, step in the therapeutic journey of a disease [...]

It is challenging to differentiate between tuberculous peritonitis and peritoneal carcinomatosis due to their insidious nature and intersecting symptoms. Computed tomography (CT) is the modality of choice in evaluating diffuse peritoneal disease. We conducted an ambispective analysis of patients suspected as having tuberculous peritonitis or peritoneal tuberculosis between Jan 2020 to Dec 2021. The study aimed to identify the clinical and radiological features differentiating the two entities. We included 44 cases of tuberculous peritonitis and 45 cases of peritoneal carcinomatosis, with a median age of 31.5 (23.5–40) and 52 (46–61) years, respectively (p ≤ 0.001). Fever, past history of tuberculosis, and loss of weight were significantly associated with tuberculous peritonitis (p ≤ 0.001, p = 0.038 and p = 0.001). Pain in the abdomen and history of malignancy were significantly associated with peritoneal carcinomatosis (p = 0.038 and p ≤ 0.001). Ascites was the most common radiological finding. Loculated ascites, splenomegaly and conglomeration of lymph nodes predicted tuberculous peritonitis significantly (p ≤ 0.001, p = 0.010, p = 0.038). Focal liver lesion(s) and nodular omental involvement were significantly associated with peritoneal carcinomatosis (p = 0.011, p = 0.029). The use of clinical features in conjunction with radiological findings provide better diagnostic yields because of overlapping imaging findings.

The elderly population in developed countries has increased rapidly in recent years; the elderly may be at greater risk of protein deficiency due to dietary, socio-economic, dental, and physical restrictions. Therefore, to address the issue of protein deficiency in elderly people, the present study aimed to enhance the protein content of high-gluten flour noodles, an Asian staple food, by supplementing them with soybean protein isolate (SPI) powder. The effect of SPI addition (5–20%, w/w) on composition, quality, texture, physical and sensory properties, and glycemic index (GI) of high-gluten flour noodles was investigated. The noodles made only from high-gluten flour served as control. In comparison to control noodles, 20% SPI noodles showed a rise in protein and moisture content from 16.17% to 30.64% and 36.06 to 44.84%, respectively. The cooking yield and cooking loss increased with an increase in SPI concentration compared to control noodles. Color characteristics analysis revealed the decreasing trend in brightness and yellowness of SPI noodles with minimal L* and b* values at a 20% SPI concentration. The addition of SPI also resulted in a decrease in the hardness and tensile strength of the noodles. The sensory analysis showed that 5% SPI noodles were more similar to control noodles in terms of flavor, taste, and overall acceptability. Moreover, the addition of SPI to the noodles significantly decreased the GI of the noodles reaching the standard of low-GI food. The findings of the current study indicate that soy protein noodles, besides supplementing the desired nutrients, may also prevent the risk of diabetes in elderly people.

The rising climate change concerns over the excessive exploitation of non-renewable sources have necessitated the need for alternative renewable and eco-friendly resources for the production of innovative materials, achieving the targets of bioeconomy. Lignocellulosic biomass (LB) constituted by polymeric sugars and lignin is an abundantly available plant-based renewable material in the form of agro-industrial food waste and crop residues that can be exploited for the production of an array of value-added chemicals and bioproducts. Cellulose is the most abundant natural and biodegradable organic polymer on earth derived from LB, with wide scale applications in the lignocellulosic biorefineries and food industries. The negative effects of food waste from agro-industrial activities could be reduced through the recovery of cellulose from these wastes and converting it into valuable forms. However, the extraction of cellulose from LB is a difficult task owing to the recalcitrant nature of biomass that restricts the easy accessibility of cellulose for value addition. Therefore, a suitable cellulose extraction method through the fractionation of LB is necessary for a better cellulose yield. Furthermore, owing to the enormous potential of nanocellulose (NC), researchers are keenly interested in developing ecologically acceptable cellulose extraction methods. Cellulose nanofibrils and nanocrystals confer excellent mechanical properties, non-toxic characteristics and biodegradability, due to which they possess wide-scale applications in diverse industrial sectors. The current review emphasizes the potential role of cellulose extraction and NC production from agro-food waste. The different pretreatment methods for their extraction from LB are outlined. The applications of nanocellulose in different areas are also discussed. The review also highlights the recent trends, challenges and future directions in the development of cellulose and NC-based commercial products.

Bioactive peptides derived from fish the byproduct protein hydrolysate have wide potential as functional food ingredients. The preparation of bioactive peptides is commonly achieved via enzymatic hydrolysis; this is the most preferred method because it has high specificity, fewer residual organic solvents in the product, and it is usually carried out in mild conditions. The use of various enzymes such as proteases is widely practiced in the industry, yet there are various limitations as it is of high cost and there is a limited availability of food-grade enzymes in the market. Moreover, high-throughput purification and the identification analysis of these peptides are currently being studied to further understand the functionality and characterization of the bioactive peptides. This review mainly focuses on the novel bioactive peptides derived from fish protein hydrolysates from various fish wastes and byproducts. The hydrolysis conditions, source of hydrolysate, and amino acid sequence of these novel peptides are presented, along with their corresponding methods of analysis in purification and identification. The use of various enzymes yields novel peptides with potent bioactivities, such as antiproliferative, antimicrobial, antihypertensive, antiglycemic, antitumor, and antioxidative biological functions. The increasing interest in proteomics in marine and aquatic waste utilization continues due to these products’ bioactivity and sustainability.

Gastrointestinal tuberculosis (GITB) and Crohn’s disease (CD) are close mimics. This prospective study aimed to evaluate the diagnostic performance of perfusion computed tomography (CT) in differentiating GITB from CD. Consecutive patients with ileocaecal thickening underwent perfusion CT of the ileocaecal region between January 2019 and July 2020. Two radiologists (blinded to the final diagnosis) independently assessed blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability at perfusion CT. These parameters were compared among the patients with GITB as well as active and inactive CD. Receiver operating characteristic curves were utilized for determining the diagnostic performance of perfusion CT. Interclass correlation coefficient and Bland–Altman analysis were performed to compare the observations of the two radiologists. During the study period, 34 patients underwent perfusion CT. Eight patients had diagnoses other than intestinal tuberculosis or CD. Thus, 26 patients (mean age 36 ± 14 years, 18 males) with GITB (n = 11), active CD (n = 6), and inactive CD (n = 9) were evaluated. BF, MTT, and permeability showed significant differences among the groups, while BV did not differ significantly among the groups. BF and permeability had 100% sensitivity and 100% specificity, while MTT had 61.5–100% sensitivity and 70–100% specificity for differentiating GITB from active CD and active from inactive CD. The interclass correlation coefficient for perfusion CT parameters was 0.88–1. Perfusion CT is a novel imaging technique that can improve the diagnostic performance of differentiating tuberculosis from CD.

Energy and electricity are critical prerequisites for every nation and critical components of social and economic growth. The monetary policy economist has been debating the issue of relying on core inflation or headline inflation, which includes energy, because the energy price is so volatile that it obscures the trend in inflation and dilutes the objectives of monetary policy. This paper aims at analyzing the impact of the twin deficit on energy inflation within a linear and nonlinear framework in India using time series data covering the period from 1971 to 2021. ARDL and NARDL approaches are employed to investigate the linear/symmetric response of energy inflation due to the twin-deficit in India. Estimates show a negative relationship between the twin-deficit and energy inflation in a symmetric model. The results of the asymmetric model reveal that the response of energy inflation due to expansionary fiscal policy (increment in CFD) and contractionary fiscal policy (decline in CFD) is diverse, in terms of size of the coefficient. Further, estimates of NARDL show the distinct impact of increases and decreases in CAD on energy inflation. The study offers several implications for policymakers and energy economists.

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India’s economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere’s natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World.

Water gets polluted due to the dumping of untreated industrial waste into bodies of water, particularly those containing heavy metals and dyes. Industrial water contains both inorganic and organic wastes. Numerous adsorbents that are inexpensive and easily available can be used to address the issue of water deterioration. This review report is focused on polyacrylonitrile as an efficient constituent of adsorbents to extract toxic ions and dyes. It discusses the various formulations of polyacrylonitrile, such as ion exchange resins, chelating resins, fibers, membranes, and hydrogels, synthesized through different polymerization methods, such as suspension polymerization, electrospinning, grafting, redox, and emulsion polymerization. Moreover, regeneration of adsorbent and heavy metal ions makes the adsorption process more cost-effective and efficient. The literature reporting successful regeneration of the adsorbent is included. The factors affecting the performance and outcomes of the adsorption process are also discussed.

Aeronautical ad hoc network (AANET) has been considered a promising candidate to complete the vision of “Internet in the sky” by supporting high-speed broadband connections on airplanes for 6G networks. However, the specific characteristics of AANET restrict the applicability of conventional topology and routing management algorithms. Here, these conventional methodologies reduce the packet delivery success of AANET with higher transfer delay. At that point, the artificial intelligence (AI)-driven solutions have been adapted to AANET to provide intelligent frameworks and architectures to cope with the high complexity. The AI-driven AANET can provide intelligent topology formation, sustainability, and routing management decisions in an automated fashion by considering its specific characteristics during the learning operations. More clearly, AI-driven AANETs support intelligent management architectures, overcoming conventional methodologies’ drawbacks. Although AI-based management approaches are widely used in terrestrial networks, there is a lack of a comprehensive study that supports AI-driven solutions for AANETs. To this end, this article explores the possible utilization of primary AI methodologies on the road to AI-driven AANET. Specifically, the article addresses unsupervised, supervised, and reinforcement learning as primary AI methodologies to enable intelligent AANET topology formation, sustainability, and routing management. Here, we identify the challenges and opportunities of these primary AI methodologies during the execution of AANET management. Furthermore, we discuss the critical issue of security in AANET before providing open issues.

Heavy metal is released from many industries into water. Before the industrial wastewater is discharged, the contamination level should be reduced to meet the recommended level as prescribed by the local laws of a country. They may be poisonous or cancerous in origin. Their presence does not only damage people, but also animals and vegetation because of their mobility, toxicity, and non-biodegradability into aquatic ecosystems. The review comprehensively discusses the progress made by various adsorbents such as natural materials, synthetic, agricultural, biopolymers, and commercial for extraction of the metal ions such as Ni2+, Cu2+, Pb2+, Cd2+, As2+ and Zn2+ along with their adsorption mechanisms. The adsorption isotherm indicates the relation between the amount adsorbed by the adsorbent and the concentration. The Freundlich isotherm explains the effective physical adsorption of the solute particle from the solution on the adsorbent and Langmuir isotherm gives an idea about the effect of various factors on the adsorption process. The adsorption kinetics data provide valuable insights into the reaction pathways, the mechanism of the sorption reaction, and solute uptake. The pseudo-first-order and pseudo-second-order models were applied to describe the sorption kinetics. The presented information can be used for the development of bio-based water treatment strategies.

The present work demonstrates the development of hydroxyapatite (HA)/gold (Au) nanocomposites to increase the adsorption of methylene blue (MB) dye from the wastewater. HA nanopowder was prepared via a wet chemical precipitation method by means of Ca(OH)2 and H3PO4 as starting materials. The biosynthesis of gold nanoparticles (AuNPs) has been reported for the first time by using the plant extract of Acrocarpus fraxinifolius. Finally, the as-prepared HA nanopowder was mixed with an optimized AuNPs solution to produce HA/Au nanocomposite. The prepared HA/Au nanocomposite was studied by using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and Scanning Electron Microscopy (SEM) with Energy Dispersive X-Ray Analysis (EDX) analysis. Adsorption studies were executed by batch experiments on the synthesized composite. The effect of the amount of adsorbent, pH, dye concentration and temperature was studied. Pseudo-first-order and pseudo-second-order models were used to fit the kinetic data and the kinetic modeling results reflected that the experimental data is perfectly matched with the pseudo-first-order kinetic model. The dye adsorbed waste materials have also been investigated against Pseudomonas aeruginosa, Micrococcus luteus, and Staphylococcus aureus bacteria by the agar well diffusion method. The inhibition zones of dye adsorbed samples are more or less the same as compared to as-prepared samples. The results so obtained indicates the suitability of the synthesized sample to be exploited as an adsorbent for effective treatment of MB dye from wastewater and dye adsorbed waste as an effective antibacterial agent from an economic point of view.

The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.

Energy efficiency is a major concern in the emerging mobile cellular wireless networks since massive connectivity is to be expected with high energy requirements from the network operators. Non-orthogonal multiple access (NOMA) being the frontier multiple access scheme for 5G, there exists numerous research attempts on enhancing the energy efficiency of NOMA enabled wireless networks while maintaining its outstanding performance metrics such as high throughput, data rates and capacity maximized optimally.The concept of green NOMA is introduced in a generalized manner to identify the energy efficient NOMA schemes. These schemes will result in an optimal scenario in which the energy generated for communication is managed sustainably. Hence, the effect on the environment, economy, living beings, etc is minimized. The recent research developments are classified for a better understanding of areas which are lacking attention and needs further improvement. Also, the performance comparison of energy efficient, NOMA schemes against conventional NOMA is presented. Finally, challenges and emerging research trends, for energy efficient NOMA are discussed.

Smart homes represent one of the principal points in the new ecosystem of the Internet of Things (IoT), both for the centrality of the home in the life of individuals and the significant potential concerning the diffusion of smart objects and innovative services. While IoT-oriented smart homes can revise how inhabitants interact with the domestic environment, each well-defined piece of technology necessitates precise network performance and distinct levels of security based on the sensitivity of the controlled system and the information it handles. This editorial presents a review of the papers accepted in the special issue. The issue has focused at obtaining high-quality papers aimed at solving well-known technical problems and challenges typical of IoT-oriented smart homes.

The JPEG-XR encoding process utilizes two types of transform operations: Photo Overlap Transform (POT) and Photo Core Transform (PCT). Using the Device Porting Kit (DPK) provided by Microsoft, we performed encoding and decoding processes on JPEG XR images. It was discovered that when the quantization parameter is >1-lossy compression conditions, the resulting image displays chequerboard block artefacts, border artefacts and corner artefacts. These artefacts are due to the nonlinearity of transforms used by JPEG-XR. Typically, it is not so visible; however, it can cause problems while copying and scanning applications, as it shows nonlinear transforms when the source and the target of the image have different configurations. Hence, it is important for document image processing pipelines to take such artefacts into account. Additionally, these artefacts are most problematic for high-quality settings and appear more visible at high compression ratios. In this paper, we analyse the cause of the above artefacts. It was found that the main problem lies in the step of POT and quantization. To solve this problem, the use of a “uniform matrix” is proposed. After POT (encoding) and before inverse POT (decoding), an extra step is added to multiply this uniform matrix. Results suggest that it is an easy and effective way to decrease chequerboard, border and corner artefacts, thereby improving the image quality of lossy encoding JPEG XR than the original DPK program with no increased calculation complexity or file size.

Unmanned aerial vehicle (UAV)-enabled networks and drone communications are emerging areas of research with a key focus on attaining high throughput, elongated range, and enhanced coverage over the existing networks [...]

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios.

The Internet of Things (IoT) utilizes algorithms to facilitate intelligent applications across cities in the form of smart-urban projects. As the majority of devices in IoT are battery operated, their applications should be facilitated with a low-power communication setup. Such facility is possible through the Low-Power Wide-Area Network (LPWAN), but at a constrained bit rate. For long-range communication over LPWAN, several approaches and protocols are adopted. One such protocol is the Long-Range Wide Area Network (LoRaWAN), which is a media access layer protocol for long-range communication between the devices and the application servers via LPWAN gateways. However, LoRaWAN comes with fewer security features as a much-secured protocol consumes more battery because of the exorbitant computational overheads. The standard protocol fails to support end-to-end security and perfect forward secrecy while being vulnerable to the replay attack that makes LoRaWAN limited in supporting applications where security (especially end-to-end security) is important. Motivated by this, an enhanced LoRaWAN security protocol is proposed, which not only provides the basic functions of connectivity between the application server and the end device, but additionally averts these listed security issues. The proposed protocol is developed with two options, the Default Option (DO) and the Security-Enhanced Option (SEO). The protocol is validated through Burrows–Abadi–Needham (BAN) logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The proposed protocol is also analyzed for overheads through system-based and low-power device-based evaluations. Further, a case study on a smart factory-enabled parking system is considered for its practical application. The results, in terms of network latency with reliability fitting and signaling overheads, show paramount improvements and better performance for the proposed protocol compared with the two handshake options, Pre-Shared Key (PSK) and Elliptic Curve Cryptography (ECC), of Datagram Transport Layer Security (DTLS).

Urban networks aim at facilitating users for better experience and services through smart platforms such as the Intelligent Transportation System (ITS). ITS focuses on information acquisition, sensing, contrivance control, data processing and forwarding to ground devices via user-specific application-interfaces. The utility of ITS is further improved via the Internet of Things (IoT), which supports “Connectivity to All”. One of the key applications of IoT-ITS is urban surveillance. Current surveillance in IoT-ITS is performed via fixed infrastructure-based sensing applications which consume an excessive amount of energy leading to several overheads and failures in the network. Such issues can be overcome by the utilization of on-demand nodes, such as drones, etc. However, drones-assisted surveillance requires efficient communication setup as drones are battery operated and any extemporaneous maneuver during monitoring may result in loss of drone or complete failure of the network. The novelty in terms of network layout can be procured by the utilization of drones with LoRaWAN, which is the protocol designated for Low-Power Wide Area Networks (LPWAN). However, even this architectural novelty alone cannot ascertain the formation of fail-safe, highly resilient, low-overhead, and non-redundant network, which is additionally the problem considered in this paper. To resolve such problem, this paper uses drones as LoRaWAN gateway and proposes a communication strategy based on the area stress, resilient factor, and energy consumption that avail in the efficient localization, improved coverage and energy-efficient surveillance with lower overheads, lower redundancy, and almost zero-isolations. The proposed approach is numerically simulated and the results show that the proposed approach can conserve a maximum of 39.2% and a minimum of 12.6% of the total network energy along with an improvement in the area stress between 89.7% and 53.0% for varying number of drones over a fixed area.