Construction of a novel phagemid to produce custom DNA origami scaffolds.

DNA origami, a method for constructing nanoscale objects, relies on a long single strand of DNA to act as the 'scaffold' to template assembly of numerous short DNA oligonucleotide 'staples'. The ability to generate custom scaffold sequences can greatly benefit DNA origami design processes. Custom scaffold sequences can provide better control of the overall size of the final object and better control of low-level structural details, such as locations of specific base pairs within an object. Filamentous bacteriophages and related phagemids can work well as sources of custom scaffold DNA. However, scaffolds derived from phages require inclusion of multi-kilobase DNA sequences in order to grow in host bacteria, and those sequences cannot be altered or removed. These fixed-sequence regions constrain the design possibilities of DNA origami. Here, we report the construction of a novel phagemid, pScaf, to produce scaffolds that have a custom sequence with a much smaller fixed region of 393 bases. We used pScaf to generate new scaffolds ranging in size from 1512 to 10 080 bases and demonstrated their use in various DNA origami shapes and assemblies. We anticipate our pScaf phagemid will enhance development of the DNA origami method and its future applications

Effectiveness of Urban Farming Program in Providing Multiple Benefits to the Urban Community in Malaysia

Residents have chosen to be living in urban regions in recent years largely due to the accessibility of job opportunities and public services. These led to a fast increase in the amount of people live in urban regions and cities. As a result, a large amount of the property used for agricultural activities was transformed into factories, housing units, and highways. This also resulted in a decrease in food production, growth in food prices and food import bills as the country now relies on food imports especially rice, fruits and vegetables, that can prevent the fostering of urban farming activities and then provide beneficial information essential to form it into a more consumer friendly program. Moreover, studies on urban farming are somewhat few in Malaysia and this study can become helpful for future research. The study focused on small-scale agriculture projects, such as community gardens, and community-level programs such as community supported agriculture and farmers markets. The study found that how urban agriculture enhances community resilience and wellbeing. This is the necessity for the Malaysian urban authorities to give more appropriate identification and support to city dwellers and promote them to develop the practice of urban farming

Horseshoe regularization for wavelet-based lensing inversion

Gravitational lensing, a phenomenon in astronomy, occurs when the gravitational field of a massive object, such as a galaxy or a black hole, bends the path of light from a distant object behind it. This bending results in a distortion or magnification of the distant object's image, often seen as arcs or rings surrounding the foreground object. The Starlet wavelet transform offers a robust approach to representing galaxy images sparsely. This technique breaks down an image into wavelet coefficients at various scales and orientations, effectively capturing both large-scale structures and fine details. The Starlet wavelet transform offers a robust approach to representing galaxy images sparsely. This technique breaks down an image into wavelet coefficients at various scales and orientations, effectively capturing both large-scale structures and fine details. The horseshoe prior has emerged as a highly effective Bayesian technique for promoting sparsity and regularization in statistical modeling. It aggressively shrinks negligible values while preserving important features, making it particularly useful in situations where the reconstruction of an original image from limited noisy observations is inherently challenging. The main objective of this thesis is to apply sparse regularization techniques, particularly the horseshoe prior, to reconstruct the background source galaxy from gravitationally lensed images. By demonstrating the effectiveness of the horseshoe prior in this context, this thesis tackles the challenging inverse problem of reconstructing lensed galaxy images. Our proposed methodology involves applying the horseshoe prior to the wavelet coefficients of lensed galaxy images. By exploiting the sparsity of the wavelet representation and the noise-suppressing behavior of the horseshoe prior, we achieve well-regularized reconstructions that reduce noise and artifacts while preserving structural details. Experiments conducted on simulated lensed galaxy images demonstrate lower mean squared error and higher structural similarity with the horseshoe prior compared to alternative methods, validating its efficacy as an efficient sparse modeling technique.Les lentilles gravitationnelles se produisent lorsque le champ gravitationnel d'un objet massif dévie la trajectoire de la lumière provenant d'un objet lointain, entraînant une distorsion ou une amplification de l'image de l'objet lointain. La transformation Starlet fournit une méthode robuste pour obtenir une représentation éparse des images de galaxies, capturant efficacement leurs caractéristiques essentielles avec un minimum de données. Cette représentation réduit les besoins de stockage et de calcul, et facilite des tâches telles que le débruitage, la compression et l'extraction de caractéristiques. La distribution a priori de fer à cheval est une technique bayésienne efficace pour promouvoir la sparsité et la régularisation dans la modélisation statistique. Elle réduit de manière agressive les valeurs négligeables tout en préservant les caractéristiques importantes, ce qui la rend particulièrement utile dans les situations où la reconstruction d'une image originale à partir d'observations bruitées est difficile. Étant donné la nature mal posée de la reconstruction des images de galaxies à partir de données bruitées, l'utilisation de la distribution a priori devient cruciale pour résoudre les ambiguïtés. Les techniques utilisant une distribution a priori favorisant la sparsité ont été efficaces pour relever des défis similaires dans divers domaines. L'objectif principal de cette thèse est d'appliquer des techniques de régularisation favorisant la sparsité, en particulier la distribution a priori de fer à cheval, pour reconstruire les galaxies d'arrière-plan à partir d'images de lentilles gravitationnelles. Notre méthodologie proposée consiste à appliquer la distribution a priori de fer à cheval aux coefficients d'ondelettes des images de galaxies lentillées. En exploitant la sparsité de la représentation en ondelettes et le comportement de suppression du bruit de la distribution a priori de fer à cheval, nous obtenons des reconstructions bien régularisées qui réduisent le bruit et les artefacts tout en préservant les détails structurels. Des expériences menées sur des images simulées de galaxies lentillées montrent une erreur quadratique moyenne inférieure et une similarité structurelle plus élevée avec la distribution a priori de fer à cheval par rapport à d'autres méthodes, validant son efficacité

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Expression and in vitro characterization of herpes simplex virus 1 (HSV-1) ORF P protein

Herpes simplex virus 1 (HSV-1) unspliced 8.3 latency associated transcript (LAT), which located in the long repeat sequences, has been shown to contain at least 16 open reading frames (ORF: A-P). One of these ORF, ORF P, maps almost entirely antisense to HSV-1 neurovirulence gene, ICP34.5. Both ORF P and ICP34.5 are located in the long repeat and are antisense overlapping genes. Therefore, in ORF P deletion mutants, ICP34.5 is also deleted and thus, the characterization of ORF P mutants is almost impossible. An alternative way to analyse its function is to determine those cellular and viral proteins which interact with ORF P. During these experiments, firstly, the expression of full length Glutatione-S-transferase (GST)-ORF P fusion protein was optimised and then, using GST-pull down, it was shown that ORF P interacts with a viral and a few cellular proteins in vitro. Conclusively, ORF P might have some functions in HSV-1 replication cycle

PERSEPSI ANAK USIA 5-6 TAHUN TENTANG SURGA DAN NERAKA MELALUI ANALISIS GAMBAR DI TAMAN KANAK-KANAK AN-NAMIROH PUSAT PEKANBARU

Penelitian ini bertujuan untuk mengetahui persepsi anak usia 5-6 tahun tentang surga dan neraka melalui analisis gambar di TK An-Namiroh Pusat Pekanbaru. Penelitian ini merupakan penelitian kualitatif deskripstif dengan menggunakan wawancara, dokumentasi dan observasi sebagai teknik pengumpulan data. Teknik analisis data yang digunakan adalah analisis kualitatif dengan empat tahapan yaitu pengumpulan data, reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa persepsi anak usia 5-6 tahun tentang surga dan neraka melalui analisis gambar di TK An-Namiroh Pusat Pekanbaru sudah benar sesuai dengan konsep yang terdapat di dalam Al-Qur’an dan hadist, meskipun dengan gambaran yang sangat sederhana. Anak mempersepsikan surga sebagai tempat yang indah dan penuh kenikmatan dengan adanya gambar sungai yang mengalir, pemandangan yang indah dan pohon-pohon yang sejuk. Warna pilihan untuk surga dengan nuansa warna hijau dan biru sebagai gambaran keindahan dan kesejukan. Demikian juga persepsi anak tentang neraka digambarkan dengan api yang menyala dan nuansa warna merah sebagai gambaran panas dan kondisi yang penuh dengan siksaan

Effect dietary energy levels and feeding rates on growth and body composition of fingerling rainbow trout (Oncorhynchus mykiss)

Growth, feed conversion ratio and fillet composition of rainbow trout (initial weight of 9.99±0.109)were investigated in a 6x2 factorial design experiment employing two levels of digestible energy (DE) (2900 and 3500kcal.kg-1) and six feeding rates (1.0%, 2.0%, 3.0%, 4.0%,5.0% of the body weight,(BW) day-1 and to satiation) for 60 days. Specific growth rate (SGR) was highest at 5.0% ration in both levels of digestible energy and decreased in the satiation ration. Regardless of feeding rate, rainbow trout grew more by 35% in DE 3500 kcal kg-1, There was a significant (p<0.05) interactive effect of feeding rates and DE on weight gain and feed conversion ratio (FCR). The highest FCR was found in fish fed to satiation (19- 21%), while the lowest FCRs, were found in 4%, 3% rations in DE levels of 2900 and 3500 kcal kg-1, respectively. There was a significant increase in protein and fat levels and decrease in moisture content of fish fillet (p<0.05) as feeding rate and DE increased (p<0.05). Condition factor increased when feeding rate and DE increased (14-15%). Feeding rate and DE level proved to be the main differentiating factors in growth, FCR and fillet composition parameters. Values of SGR and FCR plotted against feeding rates allowed the optimum and maximum feeding levels to be determined, which were found to be at 4% and 3%kcal day-1 in DE levels of 2900 and 3500kcal kg-1, respectively, for the rainbow trout of 109average weight