Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate

Background Citric acid is typically produced industrially by Aspergillus niger-mediated fermentation of a sucrose-based feedstock, such as molasses. The fungus Aspergillus niger has the potential to utilise lignocellulosic biomass, such as bagasse, for industrial-scale citric acid production, but realising this potential requires strain optimisation. Systems biology can accelerate strain engineering by systematic target identification, facilitated by methods for the integration of omics data into a high-quality metabolic model. In this work, we perform transcriptomic analysis to determine the temporal expression changes during fermentation of bagasse hydrolysate and develop an evolutionary algorithm to integrate the transcriptomic data with the available metabolic model to identify potential targets for strain engineering. Results The novel integrated procedure matures our understanding of suboptimal citric acid production and reveals potential targets for strain engineering, including targets consistent with the literature such as the up-regulation of citrate export and pyruvate carboxylase as well as novel targets such as the down-regulation of inorganic diphosphatase. Conclusions In this study, we demonstrate the production of citric acid from lignocellulosic hydrolysate and show how transcriptomic data across multiple timepoints can be coupled with evolutionary and metabolic modelling to identify potential targets for further engineering to maximise productivity from a chosen feedstock. The in silico strategies employed in this study can be applied to other biotechnological goals, assisting efforts to harness the potential of microorganisms for bio-based production of valuable chemicals.

A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.

Attentional Orienting in Front and Rear Spaces in a Virtual Reality Discrimination Task

Recent studies on covert attention suggested that the visual processing of information in front of us is different, depending on whether the information is present in front of us or if it is a reflection of information behind us (mirror information). This difference in processing suggests that we have different processes for directing our attention to objects in front of us (front space) or behind us (rear space). In this study, we investigated the effects of attentional orienting in front and rear space consecutive of visual or auditory endogenous cues. Twenty-one participants performed a modified version of the Posner paradigm in virtual reality during a spaceship discrimination task. An eye tracker integrated into the virtual reality headset was used to make sure that the participants did not move their eyes and used their covert attention. The results show that informative cues produced faster response times than non-informative cues but no impact on target identification was observed. In addition, we observed faster response times when the target occurred in front space rather than in rear space. These results are consistent with an orienting cognitive process differentiation in the front and rear spaces. Several explanations are discussed. No effect was found on subjects’ eye movements, suggesting that participants did not use their overt attention to improve task performance.

Modern Paradigm Towards Potential Target Identification for Antiviral (SARS-nCoV-2) and Anticancer Lipopeptides: A Pharmacophore-Based Approach

Background: Lipopeptides are potential microbial metabolites that are abandoned with broad spectrum biopharmaceutical properties ranging from antimicrobial, antiviral and anticancer, etc. Clinical studies are not much explored beyond the experimental methods to understand drug mechanisms on target proteins at the molecular level for large molecules. Due to the less available studies on potential target proteins of lipopeptide based drugs, their potential inhibitory role for more obvious treatment on disease have not been explored in the direction of lead optimization. However, Computational approaches need to be utilized to explore drug discovery aspects on lipopeptide based drugs, which are time saving and cost-effective techniques. Methods: Here a ligand-based drug discovery approach is coupled with reverse pharmacophore-mapping for the prediction of potential targets for antiviral (SARS-nCoV-2) and anticancer lipopeptides. Web-based servers PharmMapper and Swiss Target Prediction are used for the identification of target proteins for lipopeptides surfactin and iturin produced by Bacillus subtilis. Results: The studies have given the insight to treat the diseases with next-generation large molecule therapeutics. Results also indicate the affinity for Angiotensin-Converting Enzymes (ACE) and proteases as the potential viral targets for these categories of peptide therapeutics. A target protein for the Human Papilloma Virus (HPV) has also been mapped. Conclusion: The work will further help in exploring computer-aided drug designing of novel compounds with greater efficiency where the structure of the target proteins and lead compounds are known.

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

Analysis of Overexpressed miRNA in Circulation and Cancer Tissue to Develop a Potential microRNA Panel for the Diagnosis of Colorectal Cancer

Background: Colorectal cancer (CRC) represents the world’s fourth deadly cancer, but its early diagnosis can be curative with considerable success rates. This study was aimed to identify CRC specific microRNAs (miRNAs) in tissue and serum samples to develop a miRNA-based diagnostics panel for the minimal invasive detection of CRC in early condition. Methods: By integrating four microarrays in tissue and serum samples of CRC from Gene Expression Omnibus (GEO) database, we screened out the highly expressed miRNAs in each dataset by using limma R package. Two important upregulated miRNAs namely hsa-miR-1246 and hsa-miR-1825 were overlapped in both tissue and serum samples of CRC, and were investigated to target identification, followed by functional annotation and protein- protein interaction (PPI) study for the target genes through DAVID and STRING respectively. Finally, hub target genes were retrieved by Cytoscape analysis. Results: It was shown that target genes of hsa-miR-1246 and hsa-miR-1825 were involved with core KEGG pathways (such as cAMP, PI3K-Akt and calcium signaling pathway). In addition, biological processes (such as cell adhesion and cell proliferation), cellular components (such as plasma membrane and cytosol), molecular functions (such as protein binding and metal ion binding), were mostly associated with the target genes. Their top 5 target genes were retrieved and their biological function towards tumor progression was shown using Cancer Hallmarks Analytics Tool. Conclusion: This study suggested that hsa-miR-1246 and hsa-miR-1825, as overlapped upregulated tissue and circulating miRNAs might have a vital role in the development of CRC and their five hub target genes were identified.

Transcriptional Analysis of Nuclear-Encoded Mitochondrial Genes in Eight Neurodegenerative Disorders: The Analysis of Seven Diseases in Reference to Friedreich’s Ataxia

Neurodegenerative diseases (NDDs) are challenging to understand, diagnose, and treat. Revealing the genomic and transcriptomic changes in NDDs contributes greatly to the understanding of the diseases, their causes, and development. Moreover, it enables more precise genetic diagnosis and novel drug target identification that could potentially treat the diseases or at least ease the symptoms. In this study, we analyzed the transcriptional changes of nuclear-encoded mitochondrial (NEM) genes in eight NDDs to specifically address the association of these genes with the diseases. Previous studies show strong links between defects in NEM genes and neurodegeneration, yet connecting specific genes with NDDs is not well studied. Friedreich’s ataxia (FRDA) is an NDD that cannot be treated effectively; therefore, we focused first on FRDA and compared the outcome with seven other NDDs, including Alzheimer’s disease, amyotrophic lateral sclerosis, Creutzfeldt–Jakob disease, frontotemporal dementia, Huntington’s disease, multiple sclerosis, and Parkinson’s disease. First, weighted correlation network analysis was performed on an FRDA RNA-Seq data set, focusing only on NEM genes. We then carried out differential gene expression analysis and pathway enrichment analysis to pinpoint differentially expressed genes that are potentially associated with one or more of the analyzed NDDs. Our findings propose a strong link between NEM genes and NDDs and suggest that our identified candidate genes can be potentially used as diagnostic markers and therapeutic targets.

Nanobot: Artificial Intelligence, Drug Delivery and Diagnostic Approach

The design, construction, and programming of robots with overall dimensions of less than a few micrometres, as well as the programmable assembly of nanoscale items, are all part of nanorobotics. Nanobots are the next generation of medication delivery systems, as well as the ultimate nanoelectromechanical systems. Nano bioelectronics are used as the foundation for manufacturing integrated system devices with embedded nano biosensors and actuators in the nanorobot architectural paradigm, which aids in medical target identification and drug delivery. Nanotechnology advances have made it possible to create nanosensors and actuators using nano bioelectronics and biologically inspired devices. The creation of nanobots is fascinated by both top-down and bottom-up approaches. The qualities, method of synthesis, mechanism of action, element, and application of nanobots for the treatment of nervine disorders, wound healing, cancer diagnosis study, and congenital disease were highlighted in this review. This method gives you a lot of control over the situation and helps with sickness diagnosis.