Koumiss promotes Mycobacterium bovis infection by disturbing intestinal flora and inhibiting endoplasmic reticulum stress.

Mycobacterium bovis is the causative agent of bovine tuberculosis and also responsible for serious threat to public health. Koumiss is a fermented mare's milk product, used as traditional drink. Here, we explored the effect of koumiss on gut microbiota and the host immune response against M bovis infection. Therefore, mice were treated with koumiss and fresh mare milk for 14 days before M bovis infection and continue for 5 weeks after infection. The results showed a clear change in the intestinal flora of mice treated with koumiss, and the lungs of mice treated with koumiss showed severe edema, inflammatory infiltration, and pulmonary nodules in M bovis-infected mice. Notably, we found that the content of short-chain fatty acids was significantly lower in the koumiss-treated group compared with the control group. However, the expression of endoplasmic reticulum stress and apoptosis-related proteins in the lungs of koumiss-treated mice were significantly decreased. Collectively, these findings suggest that koumiss treatment disturb the intestinal flora of, which is associated with disease severity and the possible mechanism that induces lungs pathology. Our current findings can be exploited further to establish the "gut-lung" axis which might be a novel strategy for the control of tuberculosis.

Mitochondrial Transcription Factor A Regulates Mycobacterium bovis-Induced IFN-ß Production by Modulating Mitochondrial DNA Replication in Macrophages.

BACKGROUND: Mycobacterium bovis persistently survives in macrophages by developing multiple strategies to evade host immune responses, and the early induction of interferon-ß (IFN-ß) is one of these critical strategies. The mitochondrial transcription factor A (TFAM) plays a vital role in mitochondrial DNA (mtDNA) metabolism and has been suggested to influence IFN-ß production in response to viral infection. However, its role in the production of IFN-ß by M. bovis has not been elucidated. METHODS: In the current study, we investigated the role of TFAM in the production of IFN-ß in M. bovis-infected macrophages. RESULTS: We found that knockdown of TFAM expression significantly reduced M. bovis-induced IFN-ß production, mtDNA copy numbers and cytosolic mtDNA were increased in murine macrophages with M. bovis infection, cytosolic mtDNA contributed to IFN-ß production, and TFAM was required for the increase in mtDNA copy numbers induced by M. bovis. We also observed that TFAM affected the intracellular survival of M. bovis. CONCLUSIONS: Our results suggest that TFAM plays an essential role in M. bovis-induced IFN-ß production by regulating mtDNA copy numbers. This might be a new strategy adopted by M. bovis for its intracellular survival.

Inhibition of type I interferon signaling abrogates early Mycobacterium bovis infection.

BACKGROUND: Mycobacterium bovis (M. bovis) is the principal causative agent of bovine tuberculosis; however, it may also cause serious infection in human being. Type I IFN is a key factor in reducing viral multiplication and modulating host immune response against viral infection. However, the regulatory pathways of Type I IFN signaling during M. bovis infection are not yet fully explored. Here, we investigate the role of Type I IFN signaling in the pathogenesis of M. bovis infection in mice. METHODS: C57BL/6 mice were treated with IFNAR1-blocking antibody or Isotype control 24 h before M. bovis infection. After 21 and 84 days of infection, mice were sacrificed and the role of Type I IFN signaling in the pathogenesis of M. bovis was investigated. ELISA and qRT-PCR were performed to detect the expression of Type I IFNs and related genes. Lung lesions induced by M. bovis were assessed by histopathological examination. Viable bacterial count was determined by CFU assay. RESULTS: We observed an abundant expression of Type I IFNs in the serum and lung tissues of M. bovis infected mice. In vivo blockade of Type I IFN signaling reduced the recruitment of neutrophils to the lung tissue, mediated the activation of macrophages leading to an increased pro-inflammatory profile and regulated the inflammatory cytokine production. However, no impact was observed on T cell activation and recruitment in the early acute phase of infection. Additionally, blocking of type I IFN signaling reduced bacterial burden in the infected mice as compared to untreated infected mice. CONCLUSIONS: Altogether, our results reveal that Type I IFN mediates a balance between M. bovis-mediated inflammatory reaction and host defense mechanism. Thus, modulating Type I IFN signaling could be exploited as a therapeutic strategy against a large repertoire of inflammatory disorders including tuberculosis.

PP2Ac Modulates AMPK-Mediated Induction of Autophagy in Mycobacterium bovis-Infected Macrophages.

Mycobacterium bovis (M. bovis) is the causative agent of bovine tuberculosis in cattle population across the world. Human beings are at equal risk of developing tuberculosis beside a wide range of M. bovis infections in animal species. Autophagic sequestration and degradation of intracellular pathogens is a major innate immune defense mechanism adopted by host cells for the control of intracellular infections. It has been reported previously that the catalytic subunit of protein phosphatase 2A (PP2Ac) is crucial for regulating AMP-activated protein kinase (AMPK)-mediated autophagic signaling pathways, yet its role in tuberculosis is still unclear. Here, we demonstrated that M. bovis infection increased PP2Ac expression in murine macrophages, while nilotinib a tyrosine kinase inhibitor (TKI) significantly suppressed PP2Ac expression. In addition, we observed that TKI-induced AMPK activation was dependent on PP2Ac regulation, indicating the contributory role of PP2Ac towards autophagy induction. Furthermore, we found that the activation of AMPK signaling is vital for the regulating autophagy during M. bovis infection. Finally, the transient inhibition of PP2Ac expression enhanced the inhibitory effect of TKI-nilotinib on intracellular survival and multiplication of M. bovis in macrophages by regulating the host's immune responses. Based on these observations, we suggest that PP2Ac should be exploited as a promising molecular target to intervene in host-pathogen interactions for the development of new therapeutic strategies towards the control of M. bovis infections in humans and animals.

Combinatory FK506 and Minocycline Treatment Alleviates Prion-Induced Neurodegenerative Events via Caspase-Mediated MAPK-NRF2 Pathway.

Transcription factors play a significant role during the symptomatic onset and progression of prion diseases. We previously showed the immunomodulatory and nuclear factor of activated T cells' (NFAT) suppressive effects of an immunosuppressant, FK506, in the symptomatic stage and an antibiotic, minocycline, in the pre-symptomatic stage of prion infection in hamsters. Here we used for the first time, a combinatory FK506+minocycline treatment to test its transcriptional modulating effects in the symptomatic stage of prion infection. Our results indicate that prolonged treatment with FK506+minocycline was effective in alleviating astrogliosis and neuronal death triggered by misfolded prions. Specifically, the combinatory therapy with FK506+minocycline lowered the expression of the astrocytes activation marker GFAP and of the microglial activation marker IBA-1, subsequently reducing the level of pro-inflammatory cytokines interleukin 1 beta (IL-1ß) and tumor necrosis factor alpha (TNF-α), and increasing the levels of anti-inflammatory cytokines IL-10 and IL-27. We further found that FK506+minocycline treatment inhibited mitogen-activated protein kinase (MAPK) p38 phosphorylation, NF-kB nuclear translocation, caspase expression, and enhanced phosphorylated cAMP response element-binding protein (pCREB) and phosphorylated Bcl2-associated death promoter (pBAD) levels to reduce cognitive impairment and apoptosis. Interestingly, FK506+minocycline reduced mitochondrial fragmentation and promoted nuclear factor⁻erythroid2-related factor-2 (NRF2)-heme oxygenase 1 (HO-1) pathway to enhance survival. Taken together, our results show that a therapeutic cocktail of FK506+minocycline is an attractive candidate for prolonged use in prion diseases and we encourage its further clinical development as a possible treatment for this disease.

A central role for calcineurin in protein misfolding neurodegenerative diseases.

Accumulation of misfolded/unfolded aggregated proteins in the brain is a hallmark of many neurodegenerative diseases affecting humans and animals. Dysregulation of calcium (Ca2+) and disruption of fast axonal transport (FAT) are early pathological events that lead to loss of synaptic integrity and axonal degeneration in early stages of neurodegenerative diseases. Dysregulated Ca2+ in the brain is triggered by accumulation of misfolded/unfolded aggregated proteins in the endoplasmic reticulum (ER), a major Ca2+ storing organelle, ultimately leading to neuronal dysfunction and apoptosis. Calcineurin (CaN), a Ca2+/calmodulin-dependent serine/threonine phosphatase, has been implicated in T cells activation through the induction of nuclear factor of activated T cells (NFAT). In addition to the involvement of several other signaling cascades, CaN has been shown to play a role in early synaptic dysfunction and neuronal death. Therefore, inhibiting hyperactivated CaN in early stages of disease might be a promising therapeutic strategy for treating patients with protein misfolding diseases. In this review, we briefly summarize the structure of CaN, inhibition mechanisms by which immunosuppressants inhibit CaN, role of CaN in maintaining neuronal and synaptic integrity and homeostasis and the role played by CaN in protein unfolding/misfolding neurodegenerative diseases.

Comparative Study of the Molecular Basis of Pathogenicity of M. bovis Strains in a Mouse Model.

It is widely accepted that different strains of Mycobacterium tuberculosis have variable degrees of pathogenicity and induce different immune responses in infected hosts. Similarly, different strains of Mycobacterium bovis have been identified but there is a lack of information regarding the degree of pathogenicity of these strains and their ability to provoke host immune responses. Therefore, in the current study, we used a mouse model to evaluate various factors involved in the severity of disease progression and the induction of immune responses by two strains of M. bovis isolated from cattle. Mice were infected with both strains of M. bovis at different colony-forming unit (CFU) via inhalation. Gross and histological findings revealed more severe lesions in the lung and spleen of mice infected with M. bovis N strain than those infected with M. bovis C68004 strain. In addition, high levels of interferon-γ (IFN-γ), interleukin-17 (IL-17), and IL-22 production were observed in the serum samples of mice infected with M. bovis N strain. Comparative genomic analysis showed the existence of 750 single nucleotide polymorphisms and 145 small insertions/deletions between the two strains. After matching with the Virulence Factors Database, mutations were found in 29 genes, which relate to 17 virulence factors. Moreover, we found an increased number of virulent factors in M. bovis N strain as compared to M. bovis C68004 strain. Taken together, our data reveal that variation in the level of pathogenicity is due to the mutation in the virulence factors of M. bovis N strain. Therefore, a better understanding of the mechanisms of mutation in the virulence factors will ultimately contribute to the development of new strategies for the control of M. bovis infection.

IFN-ß: A Contentious Player in Host-Pathogen Interaction in Tuberculosis.

Tuberculosis (TB) is a major health threat to the human population worldwide. The etiology of the disease is Mycobacterium tuberculosis (Mtb), a highly successful intracellular pathogen. It has the ability to manipulate the host immune response and to make the intracellular environment suitable for its survival. Many studies have addressed the interactions between the bacteria and the host immune cells as involving many immune mediators and other cellular players. Interferon-ß (IFN-ß) signaling is crucial for inducing the host innate immune response and it is an important determinant in the fate of mycobacterial infection. The role of IFN-ß in protection against viral infections is well established and has been studied for decades, but its role in mycobacterial infections remains much more complicated and debatable. The involvement of IFN-ß in immune evasion mechanisms adopted by Mtb has been an important area of investigation in recent years. These advances have widened our understanding of the pro-bacterial role of IFN-ß in host-pathogen interactions. This pro-bacterial activity of IFN-ß appears to be correlated with its anti-inflammatory characteristics, primarily by antagonizing the production and function of interleukin 1ß (IL-1ß) and interleukin 18 (IL-18) through increased interleukin 10 (IL-10) production and by inhibiting the nucleotide-binding domain and leucine-rich repeat protein-3 (NLRP3) inflammasome. Furthermore, it also fails to provoke a proper T helper 1 (Th1) response and reduces the expression of major histocompatibility complex II (MHC-II) and interferon-γ receptors (IFNGRs). Here we will review some studies to provide a paradigm for the induction, regulation, and role of IFN-ß in mycobacterial infection. Indeed, recent studies suggest that IFN-ß plays a role in Mtb survival in host cells and its downregulation may be a useful therapeutic strategy to control Mtb infection.

The role of IL-10 in Mycobacterium avium subsp. paratuberculosis infection.

Mycobacterium avium subsp. paratuberculosis (MAP) is an intracellular pathogen and is the causative agent of Johne's disease of domestic and wild ruminants. Johne's disease is characterized by chronic granulomatous enteritis leading to substantial economic losses to the livestock sector across the world. MAP persistently survives in phagocytic cells, most commonly in macrophages by disrupting its early antibacterial activity. MAP triggers several signaling pathways after attachment to pathogen recognition receptors (PRRs) of phagocytic cells. MAP adopts a survival strategy to escape the host defence mechanisms via the activation of mitogen-activated protein kinase (MAPK) pathway. The signaling mechanism initiated through toll like receptor 2 (TLR2) activates MAPK-p38 results in up-regulation of interleukin-10 (IL-10), and subsequent repression of inflammatory cytokines. The anti-inflammatory response of IL-10 is mediated through membrane-bound IL-10 receptors, leading to trans-phosphorylation and activation of Janus Kinase (JAK) family receptor-associated tyrosine kinases (TyKs), that promotes the activation of latent transcription factors, signal transducer and activators of transcription 3 (STAT3). IL-10 is an important inhibitory cytokine playing its role in blocking phagosome maturation and apoptosis. In the current review, we describe the importance of IL-10 in early phases of the MAP infection and regulatory mechanisms of the IL-10 dependent pathways in paratuberculosis. We also highlight the strategies to target IL-10, MAPK and STAT3 in other infections caused by intracellular pathogens.

Towards a more desirable dry powder inhaler formulation: large spray-dried mannitol microspheres outperform small microspheres.

PURPOSE: To investigate, for the first time, the performance of a dry powder inhaler (DPI, Aerolizer(®)) in the case of a model drug (i.e. albuterol sulphate) formulated with spray dried mannitol carrier particles with homogeneous shape and solid-state form but different sizes. METHODS: Spray dried mannitol (SDM) particles were characterized in terms of size, surface area, morphology, water content, solid-state, density and electrostatic charge by a novel approach. DPI formulations composed of SDM and albuterol sulphate (AS) were prepared and evaluated in terms of drug content homogeneity and in vitro aerosolization performance. RESULTS: All SDM particles generated similar fine particle fractions of AS. Formulations consisting of larger SDM particles demonstrated better drug content homogeneity, reduced amounts of drug loss and reduced oropharyngeal deposition. Comparing different SDM products demonstrated that SDM powders with relatively poorer flowability, wider size distributions and higher charge density generated DPI formulations with poorer drug content homogeneity and deposited higher amount of drug on the inhaler, mouthpiece adaptor and throat. DPI formulation total desirability increased linearly with the mean diameter of SDM. CONCLUSION: Particle shape and solid-state form of mannitol could dominate over carrier size, bulk density, flowability and charge in terms of determining the aerosolization behaviour of AS formulated with mannitol carrier, at least within the experimental protocols applied in the present study.

Evaluating the Sorption Kinetics of Polychlorinated Biphenyls in Powdered and Granular Activated Carbon.

Activated carbon (AC) has been applied widely in water treatment as a strong sorbent for organic contaminants and, more recently, in-situ treatment and capping for remediating legacy contaminants. In some sediment environments, the sorption kinetics onto AC may significantly impact remedial performance, particularly for large, highly hydrophobic contaminants such as PCBs, but there is limited kinetic data on such compounds. In this study, batch experiments were conducted over 52 weeks to measure PCB adsorption kinetics on 2 ACs in granular (1.1 mm diameter) and powdered (0.02 mm) form using polydimethylsiloxane (PDMS) fibers to measure aqueous concentrations over time. The experiment was conducted in glass containers with water at known PCB concentration and containing 10 mg/L natural organic matter (NOM) and activated carbon. Blanks without activated carbon were used to estimate kinetics and equilibrium uptake to PDMS and NOM. The PDMS measured aqueous concentration in AC containing slurries was then used to estimate kinetics and equilibrium uptake of the various PCBs onto the AC. Achieving equilibration of PCBs onto the powdered activated carbon (PAC) was accomplished in days to weeks, but granular activated carbon (GAC) uptake was not complete for some high molecular weight congeners in a year. The data were used to fit linear driving force models with both linear and Freundlich models of equilibrium. The models were then used to predict uptake onto powdered and granular AC during in-situ capping and treatment using the CapSim model. Slow kinetics can significantly limit the performance of granular AC in high upwelling (> 1-10 cm/day) environments. This study demonstrates the usage of polymeric passive samplers to explore sorption kinetics and equilibrium for low solubility compounds as well as the differences in performance of granular and powdered forms of AC for remediation of PCB contaminated sediment.

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

An advanced deep learning models-based plant disease detection: A review of recent research.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Corrigendum: An advanced deep learning models-based plant disease detection: a review of recent research.

[This corrects the article DOI: 10.3389/fpls.2023.1158933.].

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease.

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

Perceived risk and perceptions of COVID-19 vaccine: A survey among general public in Pakistan.

BACKGROUND: The coronavirus disease has become a global pandemic, and it continues to wreak havoc on global health and the economy. The development of vaccines may offer a potential eradication of COVID-19. This study evaluated the general knowledge, attitude, and perception of COVID-19 vaccines in the Pakistani population. METHODS: A self-reporting e-survey and questionnaire-based survey from vaccination centers of different cities of Pakistan among 502 participants were conducted. The questionnaire comprised four sections inquiring demographics, vaccination status, and perception or attitude towards the vaccine. Univariate logistic regression was applied to predict the knowledge, attitude and behavior of participants. RESULTS: The mean age of participants was 50.8±20.3 years. 53% of the participants have both doses of vaccine administered. Pain on the site of injection (49.8%) was the most common symptom, followed by asthenia (43.0%), muscle pain (29.5%), and swelling (24.5%) on the site of vaccine administration. Females complain of more symptoms than males. More severe symptoms were reported after the first dose of vaccine administration; these symptoms subsided within a week for most participants. Overall, the respondents have a positive attitude towards the vaccine. 47.4% are sure about the vaccine's efficacy, 48.6% said getting vaccinated was their own decision, and 79.9% also recommended others to get vaccinated. CONCLUSION: The study concluded that the Pakistani population has a positive attitude but inadequate knowledge towards COVID-19 vaccines. Immediate awareness and vaccination education programs should be conducted by the authorities to complete the mass vaccination schedule.

CFP10-loaded PLGA nanoparticles as a booster vaccine confer protective immunity against Mycobacterium bovis.

Introduction: The limited efficacy of BCG (bacillus Calmette-Guérin) urgently requires new effective vaccination approaches for the control of tuberculosis. Poly lactic-co-glycolic acid (PLGA) is a prevalent drug delivery system. However, the effect of PLGA-based nanoparticles (NPs) against tuberculosis for the induction of mucosal immune response is no fully elucidated. In this study, we hypothesized that intranasal immunization with culture filtrate protein-10 (CFP10)-loaded PLGA NPs (CFP10-NPs) could boost the protective immunity of BCG against Mycobacterium bovis in mice. Methods: The recombinant protein CFP10 was encapsulated with PLGA NPs to prepare CFP10-NPs by the classical water-oil-water solvent-evaporation method. Then, the immunoregulatory effects of CFP10-NPs on macrophages in vitro and on BCG-immunized mice in vivo were investigated. Results: We used spherical CFP10-NPs with a negatively charged surface (zeta-potential -28.5 ± 1.7 mV) having a particle size of 281.7 ± 28.5 nm in diameter. Notably, CFP10-NPs significantly enhanced the secretion of tumor necrosis factor α (TNF-α) and interleukin (IL)-1ß in J774A.1 macrophages. Moreover, mucosal immunization with CFP10-NPs significantly increased TNF-α and IL-1ß production in serum, and immunoglobulin A (IgA) secretion in bronchoalveolar lavage fluid (BALF), and promoted the secretion of CFP10-specific interferon-γ (IFN-γ) in splenocytes of mice. Furthermore, CFP10-NPs immunization significantly reduced the inflammatory area and bacterial load in lung tissues at 3-week post-M. bovis challenge. Conclusion: CFP10-NPs markedly improve the immunogenicity and protective efficacy of BCG. Our findings explore the potential of the airway mucosal vaccine based on PLGA NPs as a vehicle for targeted lung delivery.

Mycobacterium bovis induces mitophagy to suppress host xenophagy for its intracellular survival.

Mitophagy is a selective autophagy mechanism for eliminating damaged mitochondria and plays a crucial role in the immune evasion of some viruses and bacteria. Here, we report that Mycobacterium bovis (M. bovis) utilizes host mitophagy to suppress host xenophagy to enhance its intracellular survival. M. bovis is the causative agent of animal tuberculosis and human tuberculosis. In the current study, we show that M. bovis induces mitophagy in macrophages, and the induction of mitophagy is impaired by PINK1 knockdown, indicating the PINK1-PRKN/Parkin pathway is involved in the mitophagy induced by M. bovis. Moreover, the survival of M. bovis in macrophages and the lung bacterial burden of mice are restricted by the inhibition of mitophagy and are enhanced by the induction of mitophagy. Confocal microscopy analysis reveals that induction of mitophagy suppresses host xenophagy by competitive utilization of p-TBK1. Overall, our results suggest that induction of mitophagy enhances M. bovis growth while inhibition of mitophagy improves growth restriction. The findings provide a new insight for understanding the intracellular survival mechanism of M. bovis in the host.Abbreviations: BMDM: mouse bone marrow-derived macrophage; BNIP3: BCL2/adenovirus E1B interacting protein 3; BNIP3L/NIX: BCL2/adenovirus E1B interacting protein 3-like; BCL2L13: BCL2-like 13 (apoptosis facilitator); CCCP: carbonyl cyanide m-cholorophenyl hydrazone; FUNDC1: FUN14 domain-containing 1; FKBP8: FKBP506 binding protein 8; HCV: hepatitis C virus; HBV: hepatitis B virus; IFN: interferon; L. monocytogenes: Listeria monocytogenes; M. bovis: Mycobacterium bovis; Mtb: Mycobacterium tuberculosis; Mdivi-1: mitochondrial division inhibitor 1; PINK1: PTEN-induced putative kinase 1; TBK1: TANK-binding kinase 1; TUFM: Tu translation elongation factor, mitochondrial; TEM: transmission electron microscopy.

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation.

Plants are the primary source of food for world's population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant's survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.