Use of Machine Learning in Interactive Cybersecurity and Network Education.

Cybersecurity is a complex subject for students to pursue. Hands-on online learning through labs and simulations can help students become more familiar with the subject at security classes to pursue cybersecurity education. There are several online tools and simulation platforms for cybersecurity education. However, those platforms need more constructive feedback mechanisms, and customizable hands-on exercises for users, or they oversimplify or misrepresent the content. In this paper, we aim to develop a platform for cybersecurity education that can be used either with a user interface or command line and provide auto constructive feedback for command line practices. Moreover, the platform currently has nine levels to practice for different subjects of networking and cybersecurity and a customizable level to create a customized network structure to test. The difficulty of objectives increases at each level. Moreover, an automatic feedback mechanism is developed by using a machine learning model to warn users about their typographical errors while using the command line to practice. A trial was performed with students completing a survey before and after using the application to test the effects of auto-feedback on users' understanding of the subjects and engagement with the application. The machine learning-based version of the application has a net increase in the user ratings of almost every survey field, such as user-friendliness and overall experience.

Metabolic consequences for mice lacking Endosialin: LC-MS/MS-based metabolic phenotyping of serum from C56Bl/6J Control and CD248 knock-out mice.

INTRODUCTION: The Endosialin/CD248/TEM1 protein is expressed in adipose tissue and its expression increases with obesity. Recently, genetic deletion of CD248 has been shown to protect mice against atherosclerosis on a high fat diet. OBJECTIVES: We investigated the effect of high fat diet feeding on visceral fat pads and circulating lipid profiles in CD248 knockout mice compared to controls. METHODS: From 10 weeks old, CD248-/- and +/+ mice were fed either chow (normal) diet or a high fat diet for 13 weeks. After 13 weeks the metabolic profiles and relative quantities of circulating lipid species were assessed using ultra high performance liquid chromatography-quadrupole time-of flight mass spectrometry (UHPLC-MS) with high resolution accurate mass (HRAM) capability. RESULTS: We demonstrate a specific reduction in the size of the perirenal fat pad in CD248-/- mice compared to CD248+/+, despite similar food intake. More strikingly, we identify significant, diet-dependent differences in the serum metabolic phenotypes of CD248 null compared to age and sex-matched wildtype control mice. Generalised protection from HFD-induced lipid accumulation was observed in CD248 null mice compared to wildtype, with particular reduction noted in the lysophosphatidylcholines, phosphatidylcholines, cholesterol and carnitine. CONCLUSIONS: Overall these results show a clear and protective metabolic consequence of CD248 deletion in mice, implicating CD248 in lipid metabolism or trafficking and opening new avenues for further investigation using anti-CD248 targeting agents.

Metabolic Phenotyping Study of Mouse Brains Following Acute or Chronic Exposures to Ethanol.

The chronic and acute effect of ethanol administration on the metabolic phenotype of mouse brain was studied in a C57BL/6 mouse model of ethanol abuse using both untargeted and targeted ultra performance liquid chromatography-tandem mass spectrometry. Two experiments based on either chronic (8 week) exposure to ethanol of both male and female mice or acute exposure of male mice for 11 days, plus 2 oral gavage doses of 25% ethanol, were undertaken. Marked differences were found in amino acids, nucleotides, nucleosides, and related metabolites as well as a number of different lipids. Using untargeted metabolite profiling, acute ethanol exposure found significant decreases in several metabolites including nucleosides, fatty acids, glycerophosphocholine, and a number of phospholipids, while chronic exposure resulted in increases in several amino acids with notable decreases in adenosine, acetylcarnitine, and galactosylceramides. Similarly, targeted metabolite analysis, focusing on the hydrophilic fraction of the brain tissue extract, identified significant decreases in the metabolism of amino acids and derivatives, as well as purine degradation especially after chronic exposure to ethanol.

Fully automated sample preparation procedure to measure drugs of abuse in plasma by liquid chromatography tandem mass spectrometry.

For the analysis of drugs and pharmaceutical compounds in biological matrices, extraction procedures are typically used for LC-MS/MS analysis often requiring manual steps in sample preparation. In this study, we report a fully automated extraction method carried out by a programable liquid handler directly coupled to an LC-MS/MS system for the determination of 42 components (illicit drugs and/or metabolites) (plus 20 deuterated internal standards). The acquisition was performed in positive ionization mode with up to 15 MRM transitions per compound, each with optimized collision energy (MRM spectrum mode) to enable qualitative library searching in addition to quantitation. After placing the sample tube into the system, no further intervention was necessary: automated preparation used 50 µL of blood or plasma with 3 µL of extracted sample injected for analysis. The method was validated according to the requirements of ISO 15189. The limit of detection and quantification was 1-5 ng/mL depending on the compound. Stability experiments found that historic calibration curve data files could accurately quantify for up to 1 month with less than 20% uncertainty. Comparison to a QuEChERS method was made using patient samples providing a regression correlation R2 = 0.98 between the two methods. This approach was successfully designed to support parallel sample preparation and analysis therefore significantly increasing sample throughput and reduced cycle times. Graphical abstract ᅟ.

Endogenous and xenobiotic metabolite profiling of liver extracts from SCID and chimeric humanized mice following repeated oral administration of troglitazone.

1. Metabonomic analysis, via a combination of untargeted and targeted liquid chromatography-mass spectrometry (LC-MS) and untargeted (1)H NMR spectroscopy-based metabolite profiling, was performed on aqueous (AQ) and organic liver extracts from control (SCID) and chimeric humanized (PXB) mice dosed with troglitazone at 0, 300 and 600 mg/kg/day for seven days. 2. LC-MS analysis of AQ liver extracts showed a more "human-like" profile for troglitazone metabolites for PXB, compared with SCID, mice. 3. LC-MS detected differences in endogenous metabolites, particularly lipid species in dosed mice, including elevated triacylglycerols and 1-alkyl,2-acylglycerophosphates as well as lowered diacylglycerophosphocholines and 1-alkyl,2-acylglycerophosphocholines for PXB compared with SCID mouse liver extracts. Following drug administration changes in the relative proportions of the ions for various unsaturated fatty acids were observed for both types of mouse, some of which were specific to PXB or SCID mice. 4. (1)H NMR spectroscopy revealed that AQ PXB mouse liver extracts had elevated amounts of inosine, fumarate, creatine, aspartate, trimethylamine N-oxide, glycerophosphocholine, phosphocholine, choline, glutamine, glutamate, acetate, alanine and lactate relative to SCID mice and decreased histidine, glycogen, α- and ß-glucose, taurine, and glutathione. Increased uracil and tyrosine concentrations were detected for PXB mice on troglitazone administration. 5. Metabonomic profiling thus showed clear differences between humanized and SCID mice, including after administration of troglitazone.

Global metabolite profiling of human colorectal cancer xenografts in mice using HPLC-MS/MS.

Reversed-phase gradient LC-MS was used to perform untargeted metabonomic analysis on extracts of human colorectal cancer (CRC) cell lines (COLO 205, HT-29, HCT 116 and SW620) subcutaneously implanted into age-matched athymic nude male mice to study small molecule metabolic profiles and examine possible correlations with human cancer biopsies. Following high mass accuracy data analysis using MS and MS/MS, metabolites were identified by searching against major metabolite databases including METLIN, MASSBANK, The Human Metabolome Database, PubChem, Biospider, LipidMaps and KEGG. HT-29 and COLO 205 tumor xenografts showed a distribution of metabolites that differed from SW620 and HCT 116 xenografts (predominantly on the basis of relative differences in the amounts of amino acids and lipids detected). This finding is consistent with NMR-based analysis of human colorectal tissue, where the metabolite profiles of HT-29 tumors exhibit the greatest similarity to human rectal cancer tissue with respect to changes in the relative amounts of lipids and choline-containing compounds. As the metabolic signatures of cancer cells result from oncogene-directed metabolic reprogramming, the HT-29 xenografts in mice may prove to be a useful model to further study the tumor microenvironment and cancer biology.

Proteomics Challenges for the Assessment of Synuclein Proteoforms as Clinical Biomarkers in Parkinson's Disease.

Parkinson's disease is a complex neurodegenerative disorder resulting in a multifaceted clinical presentation which includes bradykinesia combined with either rest tremor, rigidity, or both, as well as many non-motor symptoms. The motor features of the disorder are associated with the pathological form of alpha synuclein aggregates and fibrils in Lewy bodies and loss of dopaminergic neurons in the substantia nigra. Parkinson's disease is increasingly considered as a group of underlying disorders with unique genetic, biological, and molecular abnormalities that are likely to respond differentially to a given therapeutic approach. For this reason, it is clinically challenging to treat and at present, no therapy can slow down or arrest the progression of Parkinson's disease. There is a clear unmet clinical need to develop reliable diagnostic and prognostic biomarkers. When disease-modifying treatments become available, prognostic biomarkers are required to support a definitive diagnosis and clinical intervention during the long prodromal period as no clinical implications or symptoms are observed. Robust diagnostic biomarkers would also be useful to monitor treatment response. Potential biomarkers for the sporadic form of Parkinson's disease have mostly included synuclein species (monomer, oligomer, phosphorylated, Lewy Body enriched fraction and isoforms). In this review, we consider the analysis of synuclein and its proteoforms in biological samples using proteomics techniques (immunoassay and mass spectrometry) applied to neurodegenerative disease research.

Metabolic Phenotyping Study of Mouse Brain Following Microbiome Disruption by C.difficile Colonization.

Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. In the present study, a murine model was used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the effects of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the brain metabolome for the first time. Four different metabolomic-based methods (targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS, targeted GC-MS/MS, and untargeted GC-MS) were applied, resulting in the identification of 217 unique metabolites in the brain extracts, mainly glycerophospholipids, glycerolipids, amino acids, carbohydrates, and fatty acids. Univariate and multivariate statistical analysis revealed that CDI, as well as the subsequent treatments, altered significantly several brain metabolites, probably due to gut dysbiosis, and affected the brain through the gut-brain axis. Notably, none of the therapeutic approaches completely restored the brain metabolic profile to the original, healthy, and non-infected phenotype, even after 10 days of treatment.

Metabonomic investigation of liver profiles of nonpolar metabolites obtained from alcohol-dosed rats and mice using high mass accuracy MSn analysis.

Alcoholism is a complex disorder that, in man, appears to be genetically influenced, although the underlying genes and molecular pathways are not completely known. Here, the intragastric alcohol feeding model in rodents was used together with high mass accuracy LC-MS(n) analysis to assess the metabonomic changes in nonpolar metabolite profiles for livers from control and alcohol-treated rats and mice. Ion signals with a peak area variance of less than 30% (based on repeat analysis of a pooled quality control sample analyzed throughout the batch) were submitted to multivariate statistical analysis (using principal components analysis, PCA). PCA revealed robust differences between profiles from control and alcohol-treated animals from both species. The major metabolites seen to differ between control and alcohol-treated animals were identified using high accuracy MS(n) data and verified using external search engines ( http://www.lipidmaps.org ; http://www.hmdb.ca; http://www.genome.jp/kegg/ ) and authentic standards. The main metabolite classes to show major changes in the alcoholic liver-derived samples were fatty acyls, fatty acid ethyl esters, glycerolipids, and phosphatidylethanol homologues. Significant metabolites that were up-regulated by alcohol treatment in both rat and mouse livers included fatty acyls, metabolites such as octadecatrienoic acid and eicosapentaenoic acid, a number of fatty acid ethyl esters such as ethyl arachidonate, ethyl docosahexaenoic acid, ethyl linoleate, and ethyl oleate and phosphatidylethanol (PEth) homologues (predominantly PEth 18:0/18:2 and PEth 16:0/18:2; PEth homologues are currently considered as potential biomarkers for harmful and prolonged alcohol consumption in man). A number of glycerophospholipids resulted in both up-regulation (m/z 903.7436 [M + H](+) corresponding to a triglyceride) and down-regulation (m/z 667.5296 [M + H](+) corresponding to a diglyceride). Metabolite profiles were broadly similar in both mouse and rat models. However, there were a number of significant differences in the alcohol-treated group particularly in the marked down-regulation of retinol and free cholesterol in the mouse compared to the rat. Unique markers for alcohol treatment included ethyl docosahexaenoic acid. Metabolites were identified with high confidence using predominantly negative ion MS(n) data for the fatty acyl components to match to www.lipidmaps.org MS and MS/MS databases; interpreting positive ion data needed to take into account possible adduct ions which may confound the identification of other lipid classes. The observed changes in lipid profiles were consistent with alcohol-induced liver injury in humans.

Imaging mass spectrometry using chemical inkjet printing reveals differential protein expression in human oral squamous cell carcinoma.

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a recently developed technique that generates molecular profiles usually of peptide and protein signals directly from the surface of thin tissue sections and can be coupled with automation to generate two-dimensional ion density maps. This allows specific information to be obtained on the relative abundance and spatial distribution of the analytes of interest. The technique has potential for application in many diseases including cancer with respect to elucidating the molecular pathology and identifying potential biomarkers. In this proof-of-principle study we have evaluated inkjet printing of the sinapinic acid matrix used for MALDI-IMS directly onto the surface of human oral squamous cell carcinoma biopsy specimens. This MS profiling technique produced reproducible informative chemical images for clinical pathology. Analysis of the resulting protein profiles of highly expressed protein in squamous cell carcinoma of the tongue reveals spectral features at approximately 4500 and approximately 8360 Da.

Global metabolic profiling of animal and human tissues via UPLC-MS.

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Profiling and biomarker identification in plasma from different Zucker rat strains via high mass accuracy multistage mass spectrometric analysis using liquid chromatography/mass spectrometry with a quadrupole ion trap-time of flight mass spectrometer.

High mass accuracy electrospray ionisation multistage tandem mass spectrometry (MS(n)) was applied to metabolite profiling studies on plasma samples derived from two strains of rat (the Zucker (fa/fa) obese strain and the normal wild type). Using a quadrupole ion trap time-of-flight (QIT-TOF) mass spectrometer, metabolite profiling software was applied to locate components of biological significance that could account for the differences between the two strains of rat and a formula prediction software tool was used to help identify individual components. The primary factor discriminating between the two populations was the concentration of endogenous lipids. In the Zucker (fa/fa) obese strain, the dominant ion signals and MS(n) spectra were in agreement with lysoglycerophosphocholine components such as palmitoyllysophosphatidylcholine, 1-oleoylglycerophosphocholine, 1-octadecyl-sn-glycero-3-phosphocholine and 1-stearoylglycerophosphocholine and these were found in relatively higher concentrations compared to the normal wild type. Components were identified using high mass accuracy MS(n) data, formula prediction software and by agreement with published mass spectra through internet databases, rather than using a conventional approach with authentic standards. This application shows that the use of high mass accuracy electrospray ionisation MS(n) together with a software tool can be used effectively to detect and characterise unknown analytes in complex matrices, and represents a promising approach for future profiling studies.