High‐precision attitude determination and control system design and real‐time verification for CubeSats

This paper presents the design and real‐time verification of a high‐precision and low‐cost attitude determination and control system (ADCS) for CubeSat based on a micro‐electro‐mechanical (MEMS) gyroscope. The CubeSat new missions require accurate and sophisticated ADCS with attitude drift adjustment. Moreover, designing an effective ADCS for the CubeSat poses a difficult challenge. The satellite comprises of a two‐unit CubeSat, which denotes that the ADCS is designed with small size, tight mass, and energy limitations. The ADCS has been enhanced in the former few years from fairly small resolution of 10 to around 0.6 m. This attitude drift, if not properly compensated, will cause a slow attitude information loss as the error in attitude rises between the actual and estimated. To correct the attitude adrift, the proposed system utilizes a MEMS gyroscope sensor which offers a comparative attitude to the Kalman filter for estimated attitude update. Real‐time verification and validation for the ADCS are performed through Matlab/Simulink environment and lab testing to prove the efficacy of the proposed system. Simulation of the ADCS shows accurate results with error of no more than 1°.     

Poly(o-toluidine dihydrochloride): Spectral Characterization and Synthesis of Eskolite Nanoparticles

Poly(o-toluidine?·?2HCl) and its doped polymers with Mn and Cr ions have been synthesized and characterized by FT-IR, UV–Vis. and thermogravimetrical analyses. Potassium dichromate has been used as an initiator and a dopant of chromium in CrPOT preparation. The effect of doping on thermal and optical properties of Poly(o-toluidine dihydrochloride) has been discussed.

CrPOT has been used as a precursor of Cr₂O₃ nanoparticles through thermal decomposition rout. The obtained nanoparticles have been characterized by X-ray diffraction and high resolution transmission electron microscope. The results indicated that Cr₂O₃ exists in hexagonal structure as mesoporous Eskolit with average particle size of 48?nm. Optical band gap measurements indicated that Eskolite nanoparticles have wider band gap than the bulk.     

Quality Assessment of Heated Cooking Oil,Agab, Using a Simple Newly-Developed Spectrophotometric Method

Deep fat frying is one of the most widely used cooking practices but heat treatment produces many degradation products, some of which may cause health hazards. A simple, rapid, and inexpensive method for assessment of the quality of cooking oil used for frying was developed using a spectrophotometer. Potato slices were heated in Agab oil (soybean/sunflower:1/l volume) at 180 ± 5 °C for 8 h per day for 6 consecutive days. Heated samples were collected at 15-min intervals and UV absorbance at λ = 370–400 nm was measured; samples were also analyzed for anisidine value (AV), conjugated diene formation (CD), and total polar compounds (TPC). A systematic increase of absorbance in heated oil over frying time was observed. TPC was highest (R ² = 0.99) for the correlation with heating time followed by CD (0.93) and AV (0.89). The spectrophotometric method developed in the present study to assess the quality of heated oils is simple, quick, and reliable because its results were strongly correlated with the results from the TPC.     

Inhibition of lipid oxidation in fried chips and cookies by Majorana syriaca

This study evaluates the antioxidant effectiveness of a natural extract obtained from Majorana syriaca in fried and baked foods. Majorana syriaca was extracted with ethyl acetate (yield 129 g kg^?1, dry basis) and the extract was added to refined corn oil at a concentration of 500 ppm. The oil was used in deep frying of potato chips at 185 °C and in making baked cookies. Potato chips and cookies were further subjected to accelerated oxidation at 70 °C. The protection of the frying oil was moderate as indicated by the polar content and conjugated dienes (CD) measurements, however, a remarkable improvement of the oxidative stability of the fried chips was observed through the decrease of peroxide value (PV) and CD by 59–72% and 51–79%, respectively, compared to samples with no additive. Also, the PV and CD of cookies decreased by 79% and 72%, respectively.     

Regulation of very low density lipoprotein apo B metabolism by dietary fat saturation and chain length in the guinea pig

Studies investigated the effects of dietary fatty acid composition and saturation on the regulation of very low density lipoprotein (VLDL) apo B flux, clearance, and conversion to low density lipoprotein (LDL) in guinea pigs fed semipurified diets containing 15% (w/w) corn oil (CO), lard (LA), or palm kernel oil (PK). Plasma cholesterol levels were highest with dietary PK (3.1±1.0 mmol/L) followed by LA (2.4±0.4 mmol/L) and CO (1.6±0.4 mmol/L) intake. VLDL particles were larger (P<0.05) in the LA (78±7 nm) and PK (69±10 nm) groups compared to animals fed CO (49±5 nm). VLDL-apo B fractional catabolic rates (FCR) were highest in guinea pigs fed the LA diet (P<0.05) and VLDL apo B flux, estimated from VLDL ¹²⁵I-apo B turnover kinetics, were higher in LA compared to PK or CO fed guinea pigs. In the case of PK consumption, the kinetic estimates of VLDL apo B flux significantly underestimated rates compared to direct VLDL apo B secretion measurements and LDL turnover analyses. These data demonstrate that differences in the composition and amount of saturated fatty acids have differential effects on VLDL apo B flux, catabolism, and conversion to LDL which, together with changes in LDL receptor-mediated catabolism, determine plasma LDL cholesterol levels in guinea pigs. The data also indicate that kinetic analysis of VLDL metabolism in PK fed animals is inaccurate possibly due to the presence of a small, nonequilibrating pool of newly synthesized VLDL which is rapidly converted to LDL.     

Spotlight on Exosomal Non-Coding RNAs in Breast Cancer: An In Silico Analysis to Identify Potential lncRNA/circRNA-miRNA-Target Axis

Breast cancer (BC) has recently become the most common cancer type worldwide, with metastatic disease being the main reason for disease mortality. This has brought about strategies for early detection, especially the utilization of minimally invasive biomarkers found in various bodily fluids. Exosomes have been proposed as novel extracellular vesicles, readily detectable in bodily fluids, secreted from BC-cells or BC-tumor microenvironment cells, and capable of conferring cellular signals over long distances via various cargo molecules. This cargo is composed of different biomolecules, among which are the novel non-coding genome products, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and the recently discovered circular RNA (circRNA), all of which were found to be implicated in BC pathology. In this review, the diverse roles of the ncRNA cargo of BC-derived exosomes will be discussed, shedding light on their primarily oncogenic and additionally tumor suppressor roles at different levels of BC tumor progression, and drug sensitivity/resistance, along with presenting their diagnostic, prognostic, and predictive biomarker potential. Finally, benefiting from the miRNA sponging mechanism of action of lncRNAs and circRNAs, we established an experimentally validated breast cancer exosomal non-coding RNAs-regulated target gene axis from already published exosomal ncRNAs in BC. The resulting genes, pathways, gene ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis could be a starting point to better understand BC and may pave the way for the development of novel diagnostic and prognostic biomarkers and therapeutics.     

Identification of Novel Insulin Resistance Related ceRNA Network in T2DM and Its Potential Editing by CRISPR/Cas9

Background: Type 2 diabetes mellitus is one of the leading causes of morbidity and mortality worldwide and is derived from an accumulation of genetic and epigenetic changes. In this study, we aimed to construct Insilco, a competing endogenous RNA (ceRNA) network linked to the pathogenesis of insulin resistance followed by its experimental validation in patients’, matched control and cell line samples, as well as to evaluate the efficacy of CRISPR/Cas9 as a potential therapeutic strategy to modulate the expression of this deregulated network. By applying bioinformatics tools through a two-step process, we identified and verified a ceRNA network panel of mRNAs, miRNAs and lncRNA related to insulin resistance, Then validated the expression in clinical samples (123 patients and 106 controls) and some of matched cell line samples using real time PCR. Next, two guide RNAs were designed to target the sequence flanking LncRNA/miRNAs interaction by CRISPER/Cas9 in cell culture. Gene editing tool efficacy was assessed by measuring the network downstream proteins GLUT4 and mTOR via immunofluorescence. Results: LncRNA-RP11-773H22.4, together with RET, IGF1R and mTOR mRNAs, showed significant upregulation in T2DM compared with matched controls, while miRNA (i.e., miR-3163 and miR-1) and mRNA (i.e., GLUT4 and AKT2) expression displayed marked downregulation in diabetic samples. CRISPR/Cas9 successfully knocked out LncRNA-RP11-773H22.4, as evidenced by the reversal of the gene expression of the identified network at RNA and protein levels to the normal expression pattern after gene editing. Conclusions: The present study provides the significance of this ceRNA based network and its related target genes panel both in the pathogenesis of insulin resistance and as a therapeutic target for gene editing in T2DM.     

Synthesis and characterization of crosslinked chitosan immobilized on bentonite and its grafted products with polyaniline

Chitosan immobilized bentonite (CIB) namely chitosan‐coated bentonite (5% chitosan content) was synthesized in 2% acetic acid solution, followed by crosslinking, using epichlorohydrien (ECH). The so‐obtained crosslinked chitosan immobilized on bentonite (CIB‐ECH) and CIB composites were grafted with polyaniline (PANI) through oxidative‐radical copolymerization using ammonium peroxydisulfate in acidic medium to produce PANI‐grafted crosslinked chitosan immobilized on bentonite (PANI‐g‐CIB‐ECH) and PANI‐grafted‐chitosan immobilized on bentonite (PANI‐g‐CIB) composites, respectively. The resultant composites were characterized by using X‐ray diffraction (XRD), thermo gravimetric analysis/differential scanning calorimetry, scanning electron microscopy, Fourier transform infrared (FTIR), and electrical conductivity. XRD and FTIR analyses indicate that chitosan was not intercalated into the silicate layer. Also the electrical conductivity elucidates that the grafted composites fall in the range required for the application as electrostatic dissipation. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 41078.     

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.     

On optimizing syntactic pattern recognition using tries and AI-based heuristic-search strategies.

This paper deals with the problem of estimating, using enhanced artificial-intelligence (AI) techniques, a transmitted string X* by processing the corresponding string Y, which is a noisy version of X*. It is assumed that Y contains substitution, insertion, and deletion (SID) errors. The best estimate X+ of X* is defined as that element of a dictionary H that minimizes the generalized Levenshtein distance (GLD) D (X, Y) between X and Y, for all X epsilon H. In this paper, it is shown how to evaluate D (X, Y) for every X epsilon H simultaneously, when the edit distances are general and the maximum number of errors is not given a priori, and when H is stored as a trie. A new scheme called clustered beam search (CBS) is first introduced, which is a heuristic-based search approach that enhances the well-known beam-search (BS) techniques used in AI. The new scheme is then applied to the approximate string-matching problem when the dictionary is stored as a trie. The new technique is compared with the benchmark depth-first search (DFS) trie-based technique (with respect to time and accuracy) using large and small dictionaries. The results demonstrate a marked improvement of up to 75% with respect to the total number of operations needed on three benchmark dictionaries, while yielding an accuracy comparable to the optimal. Experiments are also done to show the benefits of the CBS over the BS when the search is done on the trie. The results also demonstrate a marked improvement (more than 91%) for large dictionaries.