Flash Lamps as Ignition and Initiation Sources of the VS-2 Pyrotechnic Composition

In the present work it is found that the pyrotechnic composition VS-2 can be initiated with flash lamps IFC-500 and EVIS. VS-2 pyrotechnic composition contains 90% of mercury（Ⅱ） 5-hydrazinotetrazolate perchlorate and 10% of optically transparent copolymer of 2-methyl-5-vinyltetrazole and methacrylic acid （PVMT）. We have found that the flash lamps make it possible to initiate combustion of VS-2 composition with its transition to detonation both in cylindrical charges placed in brass caps of 5 mm diameter and 2 mm high， and film charges with 10 mm×80 mm in size and surface weights of 60 mg·cm^-2 and 90 mg·cm^-2， showing ignition delay times 10 μs and 3 μs， respectively. We also measured detonation velocities for VS-2 composition film charges， which were 4375-4505 m·s^-1 （of the charge being surface mass 60 mg·cm^-2） and 4221-4281 m·s^-1 （of the charge being surface mass 90 mg·cm^-2） and their blasting action on the aluminum plate. The depths of the normal shock wave imprints at the charge-barrier interface were 0.6-0.7 mm （for surface mass of the film charges 60 mg·cm^-2） and 1.2-1.3 mm （for surface mass of the film charges 90 mg·cm^-2）.     

Cytotoxic Potential of a-Azepano- and 3-Amino-3,4-SeCo-Triterpenoids

Semi-synthetic triterpenoids, holding an amino substituted seven-membered A-ring (azepano-ring), which could be synthesized from triterpenic oximes through a Beckmann type rearrangement followed by a reduction of lactame fragment, are considered to be novel promising agents exhibiting anti-microbial, alpha-glucosidase, and butyrylcholinesterase inhibitory activities. In this study, in an attempt to develop new antitumor candidates, a series of A-ring azepano- and 3-amino-3,4-seco-derivatives of betulin, oleanolic, ursolic, and glycyrrhetinic acids were evaluated for their cytotoxic activity against five human cancer cell lines and non-malignant mouse fibroblasts by means of a colorimetric sulforhodamine assay. Azepanoallobetulinic acid amide derivative 11 was the most cytotoxic compound of this series but showed little selectivity between the different human tumor cell lines. Flow cytometry experiments showed compound 11 to act mainly by apoptosis (44.3%) and late apoptosis (21.4%). The compounds were further screened at the National Cancer Institute towards a panel of 60 cancer cell lines. It was found that compounds 3, 4, 7, 8, 9, 11, 15, 16, 19, and 20 showed growth inhibitory (GI₅₀) against the most sensitive cell lines at submicromolar concentrations (0.20–0.94 μM), and their cytotoxic activity (LC₅₀) was also high (1–6 μM). Derivatives 3, 8, 11, 15, and 16 demonstrated a certain selectivity profile at GI₅₀ level from 5.16 to 9.56 towards K-562, CCRF-CEM, HL-60(TB), and RPMI-8226 (Leukemia), HT29 (Colon cancer), and OVCAR-4 (Ovarian cancer) cell lines. Selectivity indexes of azepanoerythrodiol 3 at TGI level ranged from 5.93 (CNS cancer cell lines SF-539, SNB-19 and SNB-75) to 14.89 for HCT-116 (colon cancer) with SI 9.56 at GI₅₀ level for the leukemia cell line K-562. The present study highlighted the importance of A-azepano-ring in the triterpenic core for the development of novel antitumor agents, and a future aim to increase the selectivity profile will thus lie in the area of modifications of azepano-triterpenic acids at their carboxyl group.     

Tissue-Specific Ferritin- and GFP-Based Genetic Vectors Visualize Neurons by MRI in the Intact and Post-Ischemic Rat Brain

(1) Background: Neurogenesis is considered to be a potential brain repair mechanism and is enhanced in stroke. It is difficult to reconstruct the neurogenesis process only from the histological sections taken from different animals at different stages of brain damage and restoration. Study of neurogenesis would greatly benefit from development of tissue-specific visualization probes. (2) Purpose: The study aimed to explore if overexpression of ferritin, a nontoxic iron-binding protein, under a doublecortin promoter can be used for non-invasive visualization of neurogenesis using magnetic resonance imaging (MRI). (3) Methods: Ferritin heavy chain (FerrH) was expressed in the adeno-associated viral backbone (AAV) under the doublecortin promoter (pDCX), specific for young neurons, in the viral construct AAV-pDCX-FerrH. Expression of the enhanced green fluorescent protein (eGFP) was used as an expression control (AAV-pDCX-eGFP). The viral vectors or phosphate-buffered saline (PBS) were injected intracerebrally into 18 adult male Sprague–Dawley rats. Three days before injection, rats underwent transient middle-cerebral-artery occlusion or sham operation. Animals were subjected to In vivo MRI study before surgery and on days 7, 14, 21, and 28 days after injection using a Bruker BioSpec 11.7 T scanner. Brain sections obtained on day 28 after injection were immunostained for ferritin, young (DCX) and mature (NeuN) neurons, and activated microglia/macrophages (CD68). Additionally, RT-PCR was performed to confirm ferritin expression. (4) Results: T2* images in post-ischemic brains of animals injected with AAV-pDCX-FerrH showed two distinct zones of MRI signal hypointensity in the ipsilesioned hemisphere starting from 14 days after viral injection—in the ischemic lesion and near the lateral ventricle and subventricular zone (SVZ). In sham-operated animals, only one zone of hypointensity near the lateral ventricle and SVZ was revealed. Immunochemistry showed that ferritin-expressing cells in ischemic lesions were macrophages (88.1%), while ferritin-expressing cells near the lateral ventricle in animals both after ischemia and sham operation were mostly mature (55.7% and 61.8%, respectively) and young (30.6% and 7.1%, respectively) neurons. RT-PCR confirmed upregulated expression of ferritin in the caudoputamen and corpus callosum. Surprisingly, in animals injected with AAV-pDCX-eGFP we similarly observed two zones of hypointensity on T2* images. Cellular studies also showed the presence of mature (81.5%) and young neurons (6.1%) near the lateral ventricle in both postischemic and sham-operated animals, while macrophages in ischemic lesions were ferritin-positive (98.2%). (5) Conclusion: Ferritin overexpression induced by injection of AAV-pDCX-FerrH was detected by MRI using T2*-weighted images, which was confirmed by immunochemistry showing ferritin in young and mature neurons. Expression of eGFP also caused a comparable reduced MR signal intensity in T2*-weighted images. Additional studies are needed to investigate the potential and tissue-specific features of the use of eGFP and ferritin expression in MRI studies.     

Structural properties of nanocomposites based on resole-type phenol-formaldehyde oligomers and detonation nanodiamonds

Using the methods of infrared spectroscopy (IRS) and X-ray photoelectron spectroscopy (XPS), it was shown that short-term high-energy machining of detonation nanodiamonds (DND) leads to structural changes in the crystal structure and functional composition of the surface layer on particles. The possibility of spontaneous formation for stable colloidal systems with a narrow size distribution of mechanically activated DND in phenol-formaldehyde oligomers (PFO) was established. By molecular spectroscopy it was revealed that π → π* interactions of the aromatic rings of PFO are caused by orientational phenomena as a result of hydrogen bonds between an activated DND surface and functional groups of PFO. The effect of DND concentration on the curing reaction parameters ofpsgr the phenol-formaldehyde oligomer was determined by differential scanning calorimetry (DSC). The concentration effect of mechanically activated nanodiamonds on the physical and mechanical characteristics of a composite material based on phenol-formaldehyde binder and polyamide paper (Nomex) was studied. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48582.     

Porous ceramics based on diatomite: Mechanical properties and evolution of morphology after deformation

Journal of Electroceramics - The work is devoted to the study of the mechanical properties of porous ceramics based on diatomite, which has high porosity, adsorption capacity, weak thermal and...     

Adipose‐Derived Biogenic Nanoparticles for Suppression of Inflammation

Extracellular vesicles secreted from adipose‐derived mesenchymal stem cells (ADSCs) have therapeutic effects in inflammatory diseases. However, production of extracellular vesicles (EVs) from ADSCs is costly, inefficient, and time consuming. The anti‐inflammatory properties of adipose tissue‐derived EVs and other biogenic nanoparticles have not been explored. In this study, biogenic nanoparticles are obtained directly from lipoaspirate, an easily accessible and abundant source of biological material. Compared to ADSC‐EVs, lipoaspirate nanoparticles (Lipo‐NPs) take less time to process (hours compared to months) and cost less to produce (clinical‐grade cell culture facilities are not required). The physicochemical characteristics and anti‐inflammatory properties of Lipo‐NPs are evaluated and compared to those of patient‐matched ADSC‐EVs. Moreover, guanabenz loading in Lipo‐NPs is evaluated for enhanced anti‐inflammatory effects. Apolipoprotein E and glycerolipids are enriched in Lipo‐NPs compared to ADSC‐EVs. Additionally, the uptake of Lipo‐NPs in hepatocytes and macrophages is higher. Lipo‐NPs and ADSC‐EVs have comparable protective and anti‐inflammatory effects. Specifically, Lipo‐NPs reduce toll‐like receptor 4‐induced secretion of inflammatory cytokines in macrophages. Guanabenz‐loaded Lipo‐NPs further suppress inflammatory pathways, suggesting that this combination therapy can have promising applications for inflammatory diseases.     

High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

Cloud computing offers internet location-based affordable, scalable, and independent services. Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events, for instance, pandemic situations. To handle massive information, clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data, with elevated velocity and modified configurations. Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server. Data deduplication also saves network bandwidth. In this paper, a new cloud-based big data security technique utilizing dual encryption is proposed. The clustering model is utilized to analyze the Deduplication process hash function. Multi kernel Fuzzy C means (MKFCM) was used which helps cluster the data stored in cloud, on the basis of confidence data encryption procedure. The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher (OSC) technique is used. This security process involving dual encryption with the optimization model develops the productivity mechanism. In this paper, the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques. The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.     

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance. The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches, the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.