Ensemble technique of intrusion detection for IoT-edge platform.

Internet of Things (IoT) technology has revolutionized modern industrial sectors. Moreover, IoT technology has been incorporated within several vital domains of applicability. However, security is overlooked due to the limited resources of IoT devices. Intrusion detection methods are crucial for detecting attacks and responding adequately to every IoT attack. Conspicuously, the current study outlines a two-stage procedure for the determination and identification of intrusions. In the first stage, a binary classifier termed an Extra Tree (E-Tree) is used to analyze the flow of IoT data traffic within the network. In the second stage, an Ensemble Technique (ET) comprising of E-Tree, Deep Neural Network (DNN), and Random Forest (RF) examines the invasive events that have been identified. The proposed approach is validated for performance analysis. Specifically, Bot-IoT, CICIDS2018, NSL-KDD, and IoTID20 dataset were used for an in-depth performance assessment. Experimental results showed that the suggested strategy was more effective than existing machine learning methods. Specifically, the proposed technique registered enhanced statistical measures of accuracy, normalized accuracy, recall measure, and stability.

Physiological and histopathological effects of polystyrene nanoparticles on the filter-feeding fish Hypophthalmichthys molitrix.

Excessive use of plastics in daily life is causing plastic pollution in aquatic environment and threatening the aquatic life. Therefore, research on the plastic pollution in aquatic environment is crucial to understand its impact and develop effective solution for safeguarding aquatic life and ecosystem. The current study investigated the effects of water borne polystyrene nanoparticles (PS-NPs) on hemato-immunological indices, serum metabolic enzymes, gills, and liver antioxidant parameters, plasma cortisol level and histopathological changes in liver and gill tissues of the widely distributed fish Hypophthalmichthys molitrix. The fingerlings of H. molitrix were exposed to different concentrations (T1-0.5, T2-1.0, and T3-2.0 mg/L) of PS-NPs respectively for 15 days consecutively. Our results indicated the dose dependent negative effects of PS-NPs on the physiology and histopathology of H. molitrix. Immuno-hematological indices showed significant increase in WBCs count, phagocytic activity, and lysozyme activity, while decreased RBC count, Hct%, Hb level, total proteins, IgM, and respiratory burst activity were observed. The levels of antioxidant enzymes like SOD, CAT and POD showed the decreasing trends while metabolic enzymes (AST, ALT, ALP and LDH), LPO, ROS activities and relative expressions of SOD1, CAT, HIF1-α and HSP-70 genes increased with increased concentrations of PS-NPs. Moreover, blood glucose and cortisol levels also showed significant increasing trends with dose dependent manner. Histopathological examination indicated moderate to severe changes in the gills and liver tissues of the group treated with 2.0 mg/L of PS-NPs. Overall, the results showed the deleterious effects of PS-NPs on physiology, immunity, metabolism, and gene expressions of H. molitrix. It is concluded that particulate plastic pollution has deleterious effects on filter feeding fish, which might affect human health through food chain and particulate chemical toxicity.

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments.

The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge-cloud deployment. A new edge-cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge-cloud deployment.

Focal dystonia and ataxic hemiparesis as the initial presentation of a thalamic tuberculoma: A diagnostic challenge in an immunocompetent pediatric patient.

Background: Central nervous system (CNS) tuberculomas are rare and account for approximately 1% of all tuberculosis (TB) cases. These intracranial lesions are more commonly observed in immunocompromised individuals, often as part of disseminated miliary TB or after latent infection reactivation. This case report presents the occurrence of a thalamic tuberculoma in an immunocompetent girl. Case Description: An 11-year-old girl presented with a 3-month history of progressive right-sided ataxic hemiparesis, hand dystonia/thalamic hand, and headache. There was only a mildly elevated erythrocyte sedimentation rate (25 mm/h.), and her remaining biochemistry and vitals were unremarkable. Magnetic resonance imaging (MRI) brain revealed an ill-defined intra-axial heterogeneous lobulated lesion with crenated margins involving the thalamus and the posterior limb of the internal capsule with significant vasogenic edema. Given the clinical picture, the working diagnosis was a high-grade brain tumor. Due to the absence of a viable operative corridor for a meaningful resection and the diagnostic uncertainty, a stereotactic biopsy was performed, and histopathological analysis confirmed the presence of granulomas consistent with TB. A human immunodeficiency virus test (negative) and interferon-gamma release assay (positive) were then obtained. The patient was commenced on a regimen of anti-TB drugs with a tapering steroid dose. At 8 months, her most recent MRI showed a significant reduction in the size of her tuberculoma, and there is a complete resolution of her hand dystonia and hemiparesis to allow for independence in her activities of daily living. Conclusion: This report emphasizes the importance of considering causes other than degenerative, vascular, or neoplasms in patients with hemiparesis with dystonia. CNS tuberculomas can present as such without prior history or specific clinical symptoms of TB, making them a diagnostic challenge. In cases with such uncertainty regarding the nature of an intracranial lesion and the role of resection, a stereotactic biopsy is invaluable.

Life table study of Sitotroga cerealella on different cereals and its implications on the performance of the egg parasitoid (Trichogramma chilonis) under laboratory conditions.

Sitotroga cerealella is one of the major pests of cereals in the field and storage conditions throughout the world. The main objective was to study the life tables of S. cerealella on wheat, maize and barley and its implications on percent parasitism of Trichogramma chilonis. S. cerealella is reared under lab conditions as its eggs are utilized for rearing T. chilonis. Fresh eggs of S. cerealella were collected and after hatching the neonate larvae of S. cerealella were transferred onto each host plant species for obtaining first (F1) generation (G). Seventy eggs were used for each host and each egg was used as a replicate. Daily observations were made for recording the life-table parameters of the S. cerealella. The data showed that the developmental time of S. cerealella eggs and pupae was maximum (5.68 and 7.75 days) when reared on wheat, while the maximum larval duration (19.77 days) of S. cerealella was recorded on barley. The maximum fecundity (290.30 ± 22.47 eggs/female) was recorded on maize, while minimum fecundity per female was recorded on barley (159.30 eggs/ female). The S. cerealella reared on maize had significantly higher values of finite rate of increase (λ), intrinsic rate of increase (r), and net reproductive rate (Ro) (0.14 ± 0.04 day- 1, 1.16 ± 0.05 day- 1, and 136.85 ± 20.25 eggs/ female) respectively. The mean generation time (T) (35.18 ± 0.61 days) was higher on wheat. Likewise, the gross reproductive rate (GRR) and the age-stage specific reproductive values (vxj) of newly oviposited eggs of S. cerealella were recorded higher (136.85 ± 20.25; 1.160 offspring) on maize. The data regarding the efficacy of T. chilonis for different parameters were recorded higher on maize i.e., percent parasitism (89.00 ± 2.30%), percent adult emergence (81.60 ± 1.20%), adult longevity (3.80 ± 0.10 days) and total adult longevity (9.90 ± 0.20 days) as compared to wheat and barley. Our findings revealed that S. cerealella can be best reared on maize under laboratory conditions as it prefers this host as compared to wheat and barley. Therefore, assigning the most susceptible and favorite host (maize) would help us to improve T. chilonis mass production under laboratory conditions.

A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19).

A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations.

Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks.

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.

Privacy in targeted advertising on mobile devices: a survey.

Targeted advertising has transformed the marketing landscape for a wide variety of businesses, by creating new opportunities for advertisers to reach prospective customers by delivering personalised ads, using an infrastructure of a number of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process, and trade a vast amount of users' personal data, which has prompted serious privacy concerns among both individuals and organisations. This article presents a comprehensive survey of the privacy risks and proposed solutions for targeted advertising in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, the measurement analysis of targeted advertising based on user's interests and profiling context, and the ads delivery process, for both in-app and in-browser targeted ads; we also include an overview of data sharing and tracking technologies. We discuss challenges in preserving the mobile user's privacy that include threats related to private information extraction and exchange among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks. Subsequently, we present various techniques for preserving user privacy and a comprehensive analysis of the proposals based on such techniques; we compare the proposals based on the underlying architectures, privacy mechanisms, and deployment scenarios. Finally, we discuss the potential research challenges and open research issues.

Integrating social media-based community of inquiry with theory of planned behavior to promote equitable educational intentions among pre-service teachers in Gilgit Baltistan, Pakistan.

An equitable education system is essential for all students to acquire the knowledge and skills necessary to become productive members of society. Pre-service teachers in education play a vital role in fostering equitable educational practices. This study aimed to measure the association between the social media-based community of inquiry and pre-service teachers' intentions toward social justice and equity in education. It focused on pre-service teachers enrolled in the education departments of universities in Gilgit Baltistan (GB), Pakistan. Census sampling was used to include all students enrolled in teacher education departments across universities in GB. The research utilized a multi-wave survey design, beginning with a baseline survey to assess pre-service teachers' presence on social media. This information guided the design of a community of inquiry on social media centered on the theme of social justice and equity in education. After 4 months, a second survey was conducted to measure the association between the community of inquiry and pre-service teachers' intentions toward social justice and equity. For data analysis, the study employed the partial least squares-consistent structural equation modeling (PLSc-SEM) approach. The novelty of the study lies in integrating the community of inquiry framework with the theory of planned behavior. We found a significant and positive association between the social media-based community of inquiry and pre-service teachers' attitudes, subjective norms, and perceived behavioral control regarding their intentions to implement social justice and equity in education. These findings hold the potential for developing prospective teachers and educational leadership with a strong focus on equity. Future research could explore creating a community of inquiry for pre-service teachers to enhance their mindset and skills for inclusive education. This aligns with the broader objective of achieving Sustainable Development Goal 4 (SDG 4) on fostering a more inclusive and equitable educational environment.

An Effective Self-Configurable Ransomware Prevention Technique for IoMT.

Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients' critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.

Distributed Blockchain-Based Platform for Unmanned Aerial Vehicles.

Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control systems must be protected to avoid potential vulnerabilities and improve coordination among scattered UAVs in the IoT context. In the current paper, a distributed UAV scheme is proposed that uses blockchain technology and a network topology similar to the IoT and cloud server to secure communications during data collection and transmission and reduce the likelihood of attack by maliciously manipulated UAVs. As an alternative to relying on a traditional blockchain approach, a unique, safe, and lightweight blockchain architecture is proposed that reduces computing and storage requirements while keeping privacy and security advantages. In addition, a unique reputation-based consensus protocol is built to assure the dependability of the decentralized network. Numerous types of transactions are established to characterize diverse data access. To validate the presented blockchain-based distributed system, performance evaluations are conducted to estimate the statistical effectiveness in the form of temporal delay, packet flow efficacy, precision, specificity, sensitivity, and security efficiency.

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey.

Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.

Role of Nose to Pelvis Imaging in the Evaluation of Foreign Body Ingestion: A Case of Magnet Ingestion in a 2-Year-Old.

Foreign body ingestion (FBI) of small-rare-earth-magnets (SREM) sets are associated with high morbidity and mortality, as these tend to cause significant mucosal injury. Current clinical guidelines for the evaluation of FBI do not include imaging of the nose and neck. A 2-year-old patient presented with known SREM ingestion, with location confirmed in the right lower quadrant on imaging at the time of initial evaluation. Subsequent imaging involving the neck revealed additional magnets lodged in the patient's hypopharynx, which were missed on initial evaluation. This case highlights the importance of considering advanced imaging of the nose and neck to uncover extraintestinal foreign bodies.

A novel IoT-fog-cloud-based healthcare system for monitoring and predicting COVID-19 outspread.

Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial-temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.

DIEER: Delay-Intolerant Energy-Efficient Routing with Sink Mobility in Underwater Wireless Sensor Networks.

Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction.

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Effects of early rearing enrichments on modulation of brain monoamines and hypothalamic-pituitary-interrenal axis (HPI axis) of fish mahseer (Tor putitora).

Enriching rearing environment is the strategy suggested for improving the post release survivorship of captive-reared animals. Here, an attempt has been made to evaluate the impact of early rearing enrichment on the hypothalamic-pituitary-interrenal axis (HPI axis), blood glucose, and brain dopaminergic and serotonergic systems of Tor putitora. Fifteen-day-old hatchlings of T. putitora were reared up to advanced fry stage in barren, semi-natural, and physically enriched environments and compared them with regard to pre-stress and post-stress levels of whole-body cortisol, blood glucose, brain serotonergic activity (5HIAA/5HT ratio), dopaminergic activity (DOPAC/DA and HVA/DA ratios) and norepinephrine (NE) levels. Significantly low basal whole-body cortisol, glucose and brain NE levels were observed in a physically enriched group of fish as compared to the other two groups. However, after acute stress, all rearing groups showed elevated levels of cortisol, blood glucose, brain 5HIAA/5HT, DOPAC/DA and HVA/DA ratios and NE levels but the magnitude of response was different among different rearing groups. The barren reared group showed a higher magnitude of response as compared to semi-natural and physically enriched groups. Similarly, the recovery rate of whole-body cortisol, blood glucose, and whole-brain monoamines were long-lasting in barren-reared mahseer. We illustrate that increased structural complexity (physical enrichment) during the early rearing significantly modulates various physiological and stress-coping mechanisms of mahseer.

Toxicological effects of furan on the reproductive system of male rats: An "in vitro" and "in vivo"-based endocrinological and spermatogonial study.

Furan is a colorless toxic chemical produced in various food items during heat processing and in chemical industries. Both in vitro and in vivo studies have reported that it induces oxidative stress and endocrine disruption; however, limited data are available regarding the effects of furan on the reproduction of mammals. In the present study, an in vitro experiment was designed to investigate the direct effects of furan exposure on oxidative stress and testosterone concentration in rat testicular tissue. Furan not only generated high oxidative stress but also decreased antioxidant enzyme activity in the testicular tissue. On the basis of in vitro study results, an in vivo sub-chronic exposure study was performed. Male rats were orally exposed to different concentrations of furan (0, 5, 10, 20, and 40 mg kg-1). An increase (P < 0.05) of reactive oxygen species (ROS) and of the lipid profile (cholesterol, triglycerides, and LDL) in higher dose treatment groups of furan was observed, while total protein content and antioxidant enzyme activity were considerably decreased after furan exposure. Also, plasma and intratesticular testosterone concentrations were reduced in high-dose treatment groups. Sperm parameters such as sperm viability, sperm count, and sperm motility showed a decrease (P < 0.05) in a dose-dependent manner. Histopathological findings revealed significant alterations in testis and epididymis tissues. These results confirm that furan can induce toxic effects on the reproductive system of male rats.

Neonatal exposure to furan alters the development of reproductive systems in adult male Sprague Dawley rats.

Furan is a colorless toxic organic compound that is produced during thermal degradation of natural food constituents, and is present in various processed foods such as coffee and processed baby foods. The present study investigated the endocrine disrupting potential of furan in Sprague Dawley male pups. On postnatal day 0 (PND 0), pups were divided into five groups. The control group received subcutaneous injections of corn oil (50 µL), while the treated groups were injected with one of four concentrations of furan (1, 5, 10 and 20 mg kg-1 d-1 in 50 µL corn oil) from PND 1 to PND 10. Our results reveal significant physiological changes in groups receiving the two highest doses of furan (10 and 20 mg kg-1 d-1). Fertility was decreased in high dose groups, as evidenced by lower daily sperm production (DSP) and epididymis sperm counts, and dose-dependent histological alterations in the testes. High dose groups showed significant reductions in plasma concentrations of testosterone, LH and GH, while plasma cortisol and final body weight was increased compared to the control group. .The results suggest that neonatal exposure to high concentrations of furan cause structural and endocrine alterations in male neonatal rats, compromising fertility.