NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION

We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.

Conservation and host-specific expression of non-tandemly repeated heterogenous ribosome RNA gene in arbuscular mycorrhizal fungi

The ribosomal RNA-encoding gene (rDNA) has a characteristic genomic nature: tens to thousands of copies in a genome, tandemly repeated structure, and intragenomic sequence homogeneity. These features contribute to ribosome productivity via physiological and evolutionary processes. We reported previously the exceptional absence of these features in the model arbuscular mycorrhizal (AM) fungus Rhizophagus irregularis. Here we examine the phylogenetic distribution of the exceptional rDNA features in the genus Rhizophagus via improving the genome sequence of R. clarus. Cross-species comparison indicated similarity of their rDNAs not only in the genomic features but also in the distribution of intragenomic polymorphic sites on the paralogs. Ribosomal RNA comprises multiple domains with different functions. The two Rhizophagus species commonly exhibited a variation enrichment site, ES27L, which is related to translational fidelity and antibiotic sensitivity. Variation enrichment on ES27L has not been observed in other organisms lacking the three rDNA features such as malaria parasites and Cyanidioschyzon merolae. Expression profiling of rDNAs in R. irregularis revealed that rDNA paralogs are expressed differently in association with host plant species. Our results suggest a broad distribution of the disarranged rDNA across AM fungi and its involvement in the successful association with the broad range of host species.

Analisis Schenkerian Pada Komposisi L’Armonia Opus 148.5 Gitar Klasik Karya Mauro Giuliani

Ada beragam jenis cara analisis musik namun yang selama ini lebih dikenal dan dipelajari di lingkungan penulis, adalah analisis bentuk musik. Ada cara lain dalam bidang analisis, salah satunya adalah analisis schenkerian. Melalui cara analisis tersebut maka akan didapatkan struktur tonal yang terdalam dari sebuah sistem musik tonal. Cara ini sama sekali belum umum di Indonesia untuk saat ini. Maka dari itu penulis ingin mempelajari lebih dalam mengenahi cara analisis schenkerian. Untuk materi pembahasan akan dibatasi pada karya Mauro Giuliani komposisi L’Armonia opus 148. 5 untuk gitar klasik. Adapun pertimbangan mengenahi objek pembahasan tersebut karena era keemasan musik tonal adalah jaman klasik. Karya tersebut dibuat pada waktu jaman klasik dan diciptakan oleh seorang komposer arus utama untuk musik instrumen gitar. Selain itu karya tersebut dimainkan dalam resital tugas akhir yang dilakukan oleh penulis. Sehingga harapan penulis dengan analisis karya Mauro Giuliani dapat melihat gambaran komponis gitar lainya pada era tersebut. Dgn menggunakan metode kualitatif desriptif dengan pendekatan musikologis, khususnya teori musik dapat disimpulkan bahwa bentuk background komposisi L’Armonia karya Mauro Giuliani adalah bentuk kedalaman yang merupakan hasil reduksi dari bentuk-bentuk sebelumnya. Di dalam bentuk ini terdapat interruption yang berfungsi sebagai penyela dan dikembalikan lagi ke kopfton 3 yang disebabkan oleh adanya struktur yang diulang. Bentuk tersebut dapat dilihat pada pembahasan background.There are various types of music analysis, but what has been better known and studied in the writer's environment, is the analysis of musical forms. There are other ways in the field of analysis, one of which is Schenkerian analysis. Through this method of analysis we will get the deepest tonal structure of a tonal music system. This method is not yet common in Indonesia at this time. Therefore the writer wants to learn more about the schenkerian analysis. For discussion material will be limited to the work of Mauro Giuliani the composition of L 'Armonia opus 148. 5 for classical guitar. The consideration of the object of discussion is because the golden era of tonal music is the classical era. The work was made in classical times and was created by a mainstream composer for guitar instrument music. In addition, the work is played in a final project recital carried out by the author. So the hope of the writer with the analysis of the work of Mauro Giuliani can see the picture of other guitar composers in that era. Using qualitative descriptive methods with a musicological approach, especially music theory, it can be concluded that the form of the background of L'AAmonia's composition by Mauro Giuliani is a form of depth that is the result of reduction from previous forms. In this form there is an interruption that functions as an interrupter and is returned again to Kopfton 3 caused by a repeated structure. This form can be seen in the background discussion.Keywords: schenkerian analysis; L'Oronia opus 148. 5.

Replicating Natures Fabric - High Information Markets and the Sustainability of Global Seafood

Misinformation currently plagues our global seafood market. Because this globally interconnected, complex and dynamic market parallels food webs and places humans as apex predators, we can apply our understanding of nature’s structure to better understand the consequences of misinformation in the global seafood system. Here, we argue that this misinformation undermines the sustainability of our global seafood system because it obscures consumers from making informed, responsible—and potentially stabilizing—decisions. it opposes a common stabilizing structure of nature’s food webs. We first describe how food webs contain a remarkably repeated structure: the generalist module, characterized by a flexible mobile generalist predator that can adapt to resource variability by making rapid smart foraging switches between resources in space. Next, we discuss how the global seafood system parallels nature’s food webs. We end by arguing that these same tools combined with proper seafood labelling have the potential to grant consumers the ability to create a high information market that allows consumers to make rapid and informed decisions. The roles that predators play in nature’s food webs strongly indicate that information is critical to aid global seafood sustainability and the maintenance of marine biodiversity.

A fucoidan from sea cucumber Pearsonothuria graeffei with well-repeated structure alleviates gut microbiota dysbiosis and metabolic syndromes in HFD-fed mice

A high-fat diet (HFD) has been a major contributor to increasing morbidity caused by metabolic syndromes.

Functional Expression and Characterization of the Recombinant N-acetyl-glucosamine/N-acetyl-galactosamine-Specific Marine Algal Lectin BPL3

Lectins, characterized by their carbohydrate-binding ability, have an extensive practical application. However, their industrial use is limited by low yields, and few active recombinant lectins have been reported. In this study, the algal lectin BPL-3 (Bryopsis plumosa lectin 3) was successfully produced using a bacterial expression system, BL21(DE3), with an artificial repeated structure (dimeric construct). Recombinant dimeric BPL3 (rD2BPL3) was confirmed by LC-MS/MS spectrometry. Expression efficiency was greater for the construct with the repeat structure (rD2BPL3) than the monomeric form (rD1BPL3). Optimal conditions for expression were 1 mM IPTG at 20 °C. Recombinant lectin was purified under denaturing conditions and refolded by the flash dilution method. Recombinant BPL3 was solubilized in 1× PBS containing 2 M urea. rD2BPL3 showed strong hemagglutination activity using human erythrocytes, similar to that of native BPL3. rD2BPL3 had a similar sugar specificity to that of the native protein, i.e., to N-acetyl-glucosamine (GlcNAc) and N-acetyl-galactosamine (GalNAc). Glycan array results showed that recombinant BPL3 and native BPL3 exhibited different binding properties. Both showed weak binding activity to α-Man-Sp. Native BPL3 showed strong binding specificity to the alpha conformation of amino sugars, and rD2BPL3 had binding activity to the beta conformation. The process developed in this study was suitable for the quality-controlled production of high amounts of soluble recombinant lectins.

CROP TYPE MAPPING FROM A SEQUENCE OF TERRASAR-X IMAGES WITH DYNAMIC CONDITIONAL RANDOM FIELDS

Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It outperforms MLC and CRFs by 7.70% and 6.42% in overall accuracy, respectively.

CROP TYPE MAPPING FROM A SEQUENCE OF TERRASAR-X IMAGES WITH DYNAMIC CONDITIONAL RANDOM FIELDS

Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It outperforms MLC and CRFs by 7.70% and 6.42% in overall accuracy, respectively.