Characterization of Urine Stem Cell-Derived Extracellular Vesicles Reveals B Cell Stimulating Cargo

Elucidation of the biological functions of extracellular vesicles (EVs) and their potential roles in physiological and pathological processes is an expanding field of research. In this study, we characterized USC–derived EVs and studied their capacity to modulate the human immune response in vitro. We found that the USC–derived EVs are a heterogeneous population, ranging in size from that of micro–vesicles (150 nm–1 μm) down to that of exosomes (60–150 nm). Regarding their immunomodulatory functions, we found that upon isolation, the EVs (60–150 nm) induced B cell proliferation and IgM antibody secretion. Analysis of the EV contents unexpectedly revealed the presence of BAFF, APRIL, IL–6, and CD40L, all known to play a central role in B cell stimulation, differentiation, and humoral immunity. In regard to their effect on T cell functions, they resembled the function of mesenchymal stem cell (MSC)–derived EVs previously described, suppressing T cell response to activation. The finding that USC–derived EVs transport a potent bioactive cargo opens the door to a novel therapeutic avenue for boosting B cell responses in immunodeficiency or cancer.     

Antioxidant properties and composition of Majorana syriaca extracts

Majorana syriaca is an important food‐flavouring ingredient in the Middle Eastern culture known commonly as Za'atar. The components of the plant were extracted in a Soxhlet apparatus sequentially with solvents of increasing polarity, specifically with petroleum ether (P), diethyl ether (D), ethyl acetate (EAc), and ethanol (E). The activity of the extracts to scavenge the 2,2‐diphenyl‐1‐picrylhydrazyl radical (DPPH^?) followed the order D > E > EAc > P. Moreover, the antioxidant activity in corn oil at concentrations of 200, 500 and 1000 ppm was monitored through peroxide value and conjugated hydroperoxide measurements and revealed that D and E extracts resulted in the highest protection followed by EAc and P extracts. Increasing the concentration from 200 to 500 ppm depressed peroxide formation, while a further increase had no effect. The main components of the P extract according to GS‐MS analysis were thymol (42.89%) and carvacrol (42.75%). The remaining extracts comprise flavonoids and phenolic acids that provide the antiradical and antioxidant activity.     

PCET based copy-move forgery detection in images under geometric transforms

With the advent of the powerful editing software and sophisticated digital cameras, it is now possible to manipulate images. Copy-move is one of the most common methods for image manipulation. Several methods have been proposed to detect and locate the tampered regions, while many methods failed when the copied region undergone some geometric transformations before being pasted, because of the de-synchronization in the searching procedure. This paper presents an efficient technique for detecting the copy-move forgery under geometric transforms. Firstly, the forged image is divided into overlapping circular blocks, and Polar Complex Exponential Transform (PCET) is employed to each block to extract the invariant features, thus, the PCET kernels represent each block. Secondly, the Approximate Nearest Neighbor (ANN) Searching Problem is used for identifying the potential similar blocks by means of locality sensitive hashing (LSH). In order to make the algorithm more robust, morphological operations are applied to remove the wrong similar blocks. Experimental results show that our proposed technique is robust to geometric transformations with low computational complexity.     

Maximal limited similarity-based rough set model

Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS-RSM. The results demonstrate that MLS-RSM model outperforms NS-RSM and can approximate the target set more efficiently.     

A Replicated Survey of IT Software Project Failures

Despite various industry reports about the failure rates of software projects, there's still uncertainty about the actual figures. Researchers performed a global Web survey of IT departments in 2005 and 2007. The results suggest that the software crisis is perhaps exaggerated and that most software projects deliver. However, the overall project failure rate, including cancelled and completed but poorly performing projects, remains arguably high for an applied discipline.     

Comparative effects of gamma and microwave irradiation on the quality of black pepper

Powdered black pepper from Egyptian markets, was irradiated with different recommended doses of gamma rays (5.0 and 10.0 kGy) and with microwaves for different periods (20, 40 and 75 s) to improve its hygienic quality. The most common bacterial isolates were of three generaBacillus, Clostridium andMicrococcus (7.5 × 10⁶), whereas the predominant fungi (7.8 × 10⁴) wereAspergillus species,A. glaucus, A. flavus, A. niger andA. ochraceus. Doses of gamma irradiation used (5.0 and 10 kGy) were sufficient to decrease spore-forming bacteria (SFB) and to inhibit the fungal flora and coliforms which contaminated the black pepper powder. Microwave treatments for 40 s and 75 s were of the same effectiveness whereas treatment for 20 s was less so. GLC analysis proved the presence of 31 peaks, only 19 compounds were identified as monoterpene hydrocarbons (56.21%), the major one being -phellandrene and limonene. Sesquiterpenes were also present, mainly -caryollphyllene (3.69%) as well as oxygenated compounds such as terpenol, geraniol, Me-chavicol, eugenol and anisol. Gamma irradiation at 5 kGy and 10 kGy respectively decreased the numbers of identified compounds from 21 (86.58% concentration) in untreated pepper to 16 (59.22% concentration), 15 (54.06% concentration). In comparison, microwave treatments, particularly for 40 s and 75 s, increased the concentration of the same compounds. The results obtained indicate that microwave treatment, under these conditions, is a safe and suitable technique for decontamination of black pepper which does not result in a great loss of flavour compounds, as compared with recommended doses of gamma irradiation.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

FPGA implementation and performance evaluation of a high throughput crypto coprocessor

This paper describes the FPGA implementation of FastCrypto, which extends a general-purpose processor with a crypto coprocessor for encrypting/decrypting data. Moreover, it studies the trade-offs between FastCrypto performance and design parameters, including the number of stages per round, the number of parallel Advance Encryption Standard (AES) pipelines, and the size of the queues. Besides, it shows the effect of memory latency on the FastCrypto performance. FastCrypto is implemented with VHDL programming language on Xilinx Virtex V FPGA. A throughput of 222 Gb/s at 444 MHz can be achieved on four parallel AES pipelines. To reduce the power consumption, the frequency of four parallel AES pipelines is reduced to 100 MHz while the other components are running at 400 MHz. In this case, our results show a FastCrypto performance of 61.725 bits per clock cycle (b/cc) when 128-bit single-port L2 cache memory is used. However, increasing the memory bus width to 256-bit or using 128-bit dual-port memory, improves the performance to 112.5 b/cc (45 Gb/s at 400 MHz), which represents 88% of the ideal performance (128 b/cc).     

Validating the ISO/IEC 15504 measure of software requirementsanalysis process capability

ISO/IEC 15504 is an emerging international standard on software process assessment. It defines a number of software engineering processes and a scale for measuring their capability. One of the defined processes is software requirements analysis (SRA). A basic premise of the measurement scale is that higher process capability is associated with better project performance (i.e., predictive validity). The paper describes an empirical study that evaluates the predictive validity of SRA process capability. Assessments using ISO/IEC 15504 were conducted on 56 projects world-wide over a period of two years. Performance measures on each project were also collected using questionnaires, such as the ability to meet budget commitments and staff productivity. The results provide strong evidence of predictive validity for the SRA process capability measure used in ISO/IEC 15504, but only for organizations with more than 50 IT staff. Specifically, a strong relationship was found between the implementation of requirements analysis practices as defined in ISO/IEC 15504 and the productivity of software projects. For smaller organizations, evidence of predictive validity was rather weak. This can be interpreted in a number of different ways: that the measure of capability is not suitable for small organizations or that the SRA process capability has less effect on project performance for small organizations     

A novel look-ahead optimization strategy for trie-based approximate string matching

This paper deals with the problem of estimating a transmitted string X ^* by processing the corresponding string Y, which is a noisy version of X ^*. We assume that Y contains substitution, insertion, and deletion errors, and that X ^* is an element of a finite (but possibly, large) dictionary, H. The best estimate X ⁺ of X ^*, is defined as that element of H which minimizes the generalized Levenshtein distance D(X, Y) between X and Y such that the total number of errors is not more than K, for all X ∈H. The trie is a data structure that offers search costs that are independent of the document size. Tries also combine prefixes together, and so by using tries in approximate string matching we can utilize the information obtained in the process of evaluating any one D(X _i , Y), to compute any other D(X _j , Y), where X _i and X _j share a common prefix. In the artificial intelligence (AI) domain, branch and bound (BB) schemes are used when we want to prune paths that have costs above a certain threshold. These techniques have been applied to prune, for example, game trees. In this paper, we present a new BB pruning strategy that can be applied to dictionary-based approximate string matching when the dictionary is stored as a trie. The new strategy attempts to look ahead at each node, c, before moving further, by merely evaluating a certain local criterion at c. The search algorithm according to this pruning strategy will not traverse inside the subtrie(c) unless there is a “hope” of determining a suitable string in it. In other words, as opposed to the reported trie-based methods (Kashyap and Oommen in Inf Sci 23(2):123–142, 1981; Shang and Merrettal in IEEE Trans Knowledge Data Eng 8(4):540–547, 1996), the pruning is done a priori before even embarking on the edit distance computations. The new strategy depends highly on the variance of the lengths of the strings in H. It combines the advantages of partitioning the dictionary according to the string lengths, and the advantages gleaned by representing H using the trie data structure. The results demonstrate a marked improvement (up to 30% when costs are of a 0/1 form, and up to 47% when costs are general) with respect to the number of operations needed on three benchmark dictionaries.