Environmental warfare

It is the middle of day, and the sky is plain black. Smoke is everywhere—black, smothering smoke. The earth is spewing burning fire that bathes the darkness with bright orange-red. Drops are falling from the sky, but not rain drops. A scene from a science fiction movie? Or maybe an artistic take on what hell might look like? No, these are the Kuwaiti oil fields burning after being set on fire by the retreating Iraqi forces following the end of the Persian Gulf War. It was an act of sabotage that was aimed to impair the Kuwaiti oil production and subsequently their economy, but it ended up causing an environmental disaster of devastating consequences. One billion barrels of oil burned to flames over the course of around 10 months. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3628

Improving diagnosis and oral vaccination strategies against bovine tuberculosis

In this work peptide antigens [ESAT-6,p45 in water (1ml, 1mg/ml)] have been adsorbed onto 10mg inorganic substrates (hydroxyapatite (MHA P201;P120, CHA), polystyrene, calcium carbonate and glass microspheres) and in vitro release characteristics were determined. The aim of formulation was to enhance the interaction of peptides with antigen presenting cells and to achieve rapid peptide release from the carrier compartment system in a mildly acidic environment. Hydroxyapatite microparticle P201 has a greater surface area and thus has the largest peptide adsorption compared to the P120. CHA gave a further higher adsorption due to larger surface area than that available on microparticles. These particles were incorporated into the BOVIGAMTM assay to determine if they improve the sensitivity. After overnight incubation the blood plasma was removed and the amount of IFN-g in each plasma sample was estimated. CHA and MHA P201 gave a significantly higher immune response at low peptide concentration compared to the free peptide, thus indicating that these systems can be used to evaluate Tuberculosis (TB) amongst cattle using the BOVIGAMTM assay. Badgers are a source of TB and pass infection to cattle. At the moment vaccination against TB in badgers is via the parenteral route and requires a trained veterinary surgeon as well as catching the badgers. This process is expensive and time consuming; consequently an oral delivery system for delivery of BCG vaccines is easier and cheaper. The initial stage involved addition of various surfactants and suspending agents to disperse BCG and the second stage involved testing for BCG viability. Various copolymers of Eudragit were used as enteric coating systems to protect BCG against the acidic environment of the stomach (SGF, 0.1M HCl pH 1.2 at 37oC) while dissolving completely in the alkaline environment of the small intestine (SIF, IM PBS solution pH 7.4 at 37oC). Eudragit L100 dispersed in 2ml PBS solution and 0.9ml Tween 80 (0.1%w/v) gave the best results remaining intact in SGF loosing only approximately 10-15% of the initial weight and dissolving completely within 3 hours. BCG was incorporated within the matrix formulation adjusted to pH 7 at the initial formulation stage containing PBS solution and Tween 80. It gave viability of x106 cfu/ml at initial formulation stage, freezing and freeze-drying stages. After this stage the matrix was compressed at 4 tons for 3 mins and placed in SGF for 2 hours and then in SIF until dissolved. The BCG viability dropped to x106 cfu/ml. There is potential to develop it further for oral delivery of BCG vaccine

Anatomical Connections of the Functionally Defined “Face Patches” in the Macaque Monkey

The neural circuits underlying face recognition provide a model for understanding visual object representation, social cognition, and hierarchical information processing. A fundamental piece of information lacking to date is the detailed anatomical connections of the face patches. Here, we injected retrograde tracers into four different face patches (PL, ML, AL, AM) to characterize their anatomical connectivity. We found that the patches are strongly and specifically connected to each other, and individual patches receive inputs from extrastriate cortex, the medial temporal lobe, and three subcortical structures (the pulvinar, claustrum, and amygdala). Inputs from prefrontal cortex were surprisingly weak. Patches were densely interconnected to one another in both feedforward and feedback directions, inconsistent with a serial hierarchy. These results provide the first direct anatomical evidence that the face patches constitute a highly specialized system and suggest that subcortical regions may play a vital role in routing face-related information to subsequent processing stages

Structural Analysis of URL For Malicious URL Detection Using Machine Learning

Malicious websites are intentionally created websites that aid online criminals in carrying out illicit actions. They commit crimes like installing malware on the victim's computer, stealing private data from the victim's system, and exposing the victim online. Malicious codes can also be found on legitimate websites. Therefore, locating such a website in cyberspace is a difficult operation that demands the utilization of an automated detection tool. Currently, machine learning/deep learning technologies are employed to detect such malicious websites. However, the problem persists since the attack vector is constantly changing. Most research solutions use a limited number of URL lexical features, DNS information, global ranking information, and webpage content features. Combining several derived features involves computation time and security risk. Additionally, the dataset's minimal features don't maximize its potential. This paper exclusively uses URLs to address this problem and blends linguistic and vectorized URL features. Complete potential of the URL is utilized through vectorization. Six machine learning algorithms are examined. The results indicate that the proposed approach performs better for the count vectorizer with random forest algorith

Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression Classifiers

One of the world's deadliest diseases is diabetes. It is an additional creator of different assortments of problems. Ex: Coronary disappointment, Visual impairment, Urinary organ illnesses, and so forth. In such cases, the patients are expected to visit a hospital to get a consultation with doctors and their reports. They must contribute their time and cash every time they visit the hospital. Yet, with the development of AI techniques, we have the adaptability to search out a response to the present problem. We have progressed an advanced framework for handling data that can figure regardless of whether the patient has polygenic sickness. In addition, being able to foresee the onset of the disease is crucial for patients. Data withdrawal has the adaptability to eliminate concealed information from an enormous amount of diabetes-related data. The most important outcomes of this research are the establishment of a theoretical framework that can reliably predict a patient's level of risk for developing diabetes. We have utilized the existing categorization methods such as DT (Decision Tree), RF (Random Forest), SVM (Support vector Machine), LR (Logistic Regression) as well as K-NN (K-Nearest Neighbors) for predicting the severity of Type-II Diabetes patients. We got an accuracy of 99% for the Random Forest, 98.40% for the Decision Tree, 78.54% for Logistic Regression, 77.94% for SVM (Using RBF Kernal SVM), and 77.64% for KNN

Colonic delivery of indometacin loaded PGA-co-PDL microparticles coated with Eudragit L100-55 from fast disintegrating tablets

The aim of this work was to investigate the efficient targeting and delivery of indometacin (IND), as a model anti-inflammatory drug to the colon for treatment of inflammatory bowel disease. We prepared fast disintegrating tablets (FDT) containing IND encapsulated within poly(glycerol-adipate-co-ɷ-pentadecalactone), PGA-co-PDL, microparticles and coated with Eudragit L100-55 at different ratios (1:1.5, 1:1, 1:0.5). Microparticles encapsulated with IND were prepared using an o/w single emulsion solvent evaporation technique and coated with Eudragit L-100-55 via spray drying. The produced coated microparticles (PGA-co-PDL-IND/Eudragit) were formulated into optimised FTD using a single station press. The loading, in vitro release, permeability and transport of IND from PGA-co-PDL-IND/Eudragit microparticles was studied in Caco-2 cell lines. IND was efficiently encapsulated (570.15 ± 4.2 μg/mg) within the PGA-co-PDL microparticles. In vitro release of PGA-co-PDL-IND/Eudragit microparticles (1:1.5) showed significantly (p < 0.05, ANOVA/Tukey) lower release of IND 13.70 ± 1.6 and 56.46 ± 3.8% compared with 1:1 (89.61 ± 2.5, 80.13 ± 2.6%) and 1:0.5 (39.46 ± 0.9 & 43.38 ± 3.12) after 3 and 43 h at pH 5.5 and 6.8, respectively. The permeability and transport studies indicated IND released from PGA-co-PDL-IND/Eudragit microparticles had a lower permeability coefficient of 13.95 ± 0.68 × 10−6cm/s compared to free IND 23.06 ± 3.56 × 10−6cm/s. These results indicate the possibility of targeting anti-inflammatory drugs to the colon using FDTs containing microparticles coated with Eudragit