Star Formation in the Trifid Nebula

We have obtained maps of the 1.25mm thermal dust emission and the molecular gas emission over a region of 20' by 10' arcmin around the Trifid Nebula (M20), with the IRAM 30m and the CSO telescopes as well as in the mid-infrared wavelength with ISO and SPITZER. Our survey is sensitive to features down to N(H2) \sim 10^{22} cm-2 in column density. The cloud material is distributed in fragmented dense gas filaments (n(H2) \sim 1000 cm-3) with sizes ranging from 1 to 10 pc. A massive filament, WF, with properties typical of Infra Red Dark Clouds, connects M20 to the W28 supernova remnant. These filaments pre-exist the formation of the Trifid and were originally self-gravitating. The fragments produced are very massive (100 Msun or more) and are the progenitors of the cometary globules observed at the border of the HII region. We could identify 33 cores, 16 of which are currently forming stars. They are usually gravitationally unbound and have low masses of a few Msun. The densest starless cores (several 10^5 cm-3) may be the site for the next generation of stars. The physical gas and dust properties of the cometary globules have been studied in detail and have been found very similar. They all are forming stars. Several intermediate-mass protostars have been detected in the cometary globules and in the deeply embedded cores. Evidence of clustering has been found in the shocked massive cores TC3-TC4-TC5. M20 is a good example of massive-star forming region in a turbulent, filamentary molecular cloud. Photoionization appears to play a minor role in the formation of the cores. The observed fragmentation is well explained by MHD-driven instabilities and is usually not related to M20. We propose that the nearby supernova remnant W28 could have triggered the formation of protostellar clusters in nearby dense cores of the Trifid.Comment: 16 pages, 24 figures, 5 Tables To appear in Astronomy and Astrophysic

Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis

The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of digital forensics research on JPEG files. Whilst there exist several challenges, this paper focuses on the challenge of determining the co-existence of JPEG fragments within various file fragment types. Existing works have exhibited a high false-positive rate, therefore rendering the need for manual validation. This study develops a technique that can identify the unique signature of JPEG 8 × 8 blocks using the Error Level Analysis technique, implemented in MATLAB. The experimental result that was conducted with 21 images of JFIF format with 1008 blocks shows the efficacy of the proposed technique. Specifically, the initial results from the experiment show that JPEG 8 × 8 blocks have unique characteristics which can be leveraged for digital forensics. An investigator could, therefore, search for the unique characteristics to identify a JPEG fragment during a digital investigation process

Resource potentials of composting the organic wastes stream from municipal solid wastes compositions arising in Nigerian cities

The paper presented an assessment of the resource potentials of composting organic waste mate-rials arising from the municipal solid waste stream from cities in Nigeria. Through a review of quantitative data, the composition of municipal solid waste arising within cities in Nigeria was examined in order to identify the potentials for composting organic materials from the waste stream composition. The data showed that the average majority of the waste stream was organic materials which implied high potentials for composting organic waste materials from Nigerian ci-ties. The review also identified further potentials for composting was associated with the large population and the majority of them engaged in agriculture, as a high potential market for com-post manure in Nigeria. There were more potentials derivable from the current policies of im-porting and supplying chemical fertilizer with huge subsidies for domestic agriculture in the country. The need to substitute these policies of chemical fertilizer with the use of compost ma-nure implied high potentials for composting in the country. The paper also identified numerous benefits of composting in general and with particular reference to the Nigerian situation which in-cludes, among others a reduction of the vast quantity of solid waste for final disposal, reducing air pollution and ground water leachate and also employment generation and increased income. Therefore, the paper recommended a deliberate government policy to promote composting of or-ganic solid wastes material and marketing of compost manure to substitute the current policies of importing chemical fertilizer with subsidy for domestic agriculture as a strategy to achieve sus-tainable waste management in Nigeria

Quantum dots improve peptide detection in MALDI MS in a size dependent manner

Laser Desorption Ionization Mass Spectrometry employs matrix which is co-crystallised with the analyte to achieve "soft ionization" that is the formation of ions without fragmentation. A variety of matrix-free and matrix-assisted LDI techniques and matrices have been reported to date. LDI has been achieved using ultra fine metal powders (UFMPs), desorption ionisation on silicon (DIOS), sol-gel assisted laser desorption/ionization (SGALDI), as well as with common MALDI matrices such as 2,5-dihydroxy benzoic acid (DHB), 3,5-dimethoxy-4-hydroxycinnamic acid (SA), α-cyano-4-hydroxycinnamic acid (CHCA) to name a few. A variety of matrix additives have been shown to improve matrix assisted desorption, including silicon nanowires (SiNW), carbon nanotubes (CNT), metal nanoparticles and nanodots. To our knowledge no evidence exists for the application of highly fluorescent CdSe/ZnS quantum dots to enhance MALDI desorption of biological samples. Here we report that although CdSe/ZnS quantum dots on their own can not substitute matrix in MALDI-MS, their presence has a moderately positive effect on MALDI desorption, improves the signal-to-noise ratio, peak quality and increases the number of detected peptides and the overall sequence coverage

Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine

File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However, completely recovering intertwined Bifragmented JPEG images into their original form without missing any parts or data of the image is a challenging due to the intertwined case might occur with non-JPEG images such as PDF, Text, Microsoft Office or random data. In this research, a new carving framework is presented in order to address the fragmentation issues that often occur in JPEG images which is called RX_myKarve. The RX_myKarve is an extended framework from X_myKarve, which consists of the following key components: (i) an Extreme Learning Machine (ELM) neural network for clusters classification using three existing content-based features extraction (Entropy, Byte Frequency Distribution (BFD) and Rate of Change (RoC)) to improve the identification of JPEG images content and support the reassembling process; (ii) a genetic algorithm with Coherence Euclidean Distance (CED) matric and cost function to reconstruct a JPEG image from a set of deformed and fragmented clusters in the scan area. The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. The RX_myKarve is implemented as an Automatic JPEG Carver (AJC) tool in order to test and compare its performance with the state-of-the art carvers such as RevIt, myKarve and X_myKarve. It is applied to three datasets namely DFRWS (2006 and 2007) forensic challenges datasets and a new dataset to test and evaluate the AJC tool. These datasets have complex challenges that simulate particular fragmentation cases addressed in this research. The final results show that the AJC with the aid of the RX_myKarve framework outperform the X_myKarve, myKarve and RevIt. The RX_myKarve is able to completely carve 23.8% images more than X_myKarve, 45.4% images more than myKarve and 67% images more than RevIt in which AJC tool using RX_myKarve completely solves the research problem