Student-Centered Group-Based Learning System

This study developed a student-centered group-based learning system. The system requirements were gathered from relevant literature on pedagogy and WebRTC. The study identified social loafing as a major drawback of most student-centered learning groups. The system was designed using block, architectural pattern, flow-chart, use-case, sequence, class and architectural-context diagrams and the system’s application logic was implemented using ASP.NET C#; HTML, JAVASCRIPT and BOOTSTRAP for the front-end and SQL for the database, HangFire and SignalR for the reminder and texting system. SendGrid API for reminders and OpenVidu Media server for video and audio-calling. The system has been tested and proven to be effective in providing different forms of communication and structure to group-learning that reduces social loafing, and can be recommended for tertiary institutions who want to promote a better student-student relationship for improved learning.

DeepMicrobeFinder Sorts Metagenomes into Prokaryotes, Eukaryotes and Viruses, with Marine Applications

Sequence classification is valuable for reducing the complexity of metagenomes and providing a fundamental understanding of the composition of metagenomic samples. Binary metagenomic classifiers offer an insufficient solution because metagenomes of most natural environments are typically derived from multiple sequence sources including prokaryotes, eukaryotes and the viruses of both. Here we introduce a deep-learning based (not reference-based) sequence classifier, DeepMicrobeFinder, that classifies metagenomic contigs into five sequence classes, e.g., viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. At different sequence lengths, DeepMicrobeFinder achieved area under the receiver operating characteristic curve (AUC) scores >0.9 for most sequence classes, the exception being distinguishing prokaryotic chromosomes from plasmids. By benchmarking on 20 test datasets with variable sequence class composition, we showed that DeepMicrobeFinder obtained average accuracy scores of ~0.94, ~0.87, and ~0.92 for eukaryotic, plasmid and viral contig classification respectively, which were significantly higher than the other state-of-the-art individual predictors. Using a 1-300 µm daily time-series metagenomic dataset sampled from coastal Southern California as a case study, we showed that metagenomic read proportions recruited by eukaryotic contigs could be doubled with DeepMicrobeFinder’s classification compared to the counterparts of other reference-based classifiers. In addition, a positive correlation could be observed between eukaryotic read proportions and potential prokaryotic community growth rates, suggesting an enrichment of fast-growing copiotrophs with increased eukaryotic particles. With its inclusive modeling and unprecedented performance, we expect DeepMicrobeFinder will promote metagenomic studies of under-appreciated sequence types.

DeepMicrobeFinder sorts metagenomes into prokaryotes, eukaryotes and viruses, with marine applications

Sequence classification is valuable for reducing the complexity of metagenomes and providing a fundamental understanding of the composition of metagenomic samples. Binary metagenomic classifiers offer an insufficient solution because metagenomes of most natural environments are typically derived from multiple sequence sources including prokaryotes, eukaryotes and the viruses of both. Here we introduce a deep-learning based (not reference-based) sequence classifier, DeepMicrobeFinder, that classifies metagenomic contigs into five sequence classes, e.g., viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. At different sequence lengths, DeepMicrobeFinder achieved area under the receiver operating characteristic curve (AUC) scores >0.9 for most sequence classes, the exception being distinguishing prokaryotic chromosomes from plasmids. By benchmarking on 20 test datasets with variable sequence class composition, we showed that DeepMicrobeFinder obtained average accuracy scores of ~0.94, ~0.87, and ~0.92 for eukaryotic, plasmid and viral contig classification respectively, which were significantly higher than the other state-of-the-art individual predictors. Using a 1-300 μm daily time-series metagenomic dataset sampled from coastal Southern California as a case study, we showed that metagenomic read proportions recruited by eukaryotic contigs could be doubled with DeepMicrobeFinder's classification compared to the counterparts of other reference-based classifiers. In addition, a positive correlation could be observed between eukaryotic read proportions and potential prokaryotic community growth rates, suggesting an enrichment of fast-growing copiotrophs with increased eukaryotic particles. With its inclusive modeling and unprecedented performance, we expect DeepMicrobeFinder will be a useful addition to the toolbox of microbial ecologists, and will promote metagenomic studies of under-appreciated sequence types.

A sequence-based global map of regulatory activity for deciphering human genetics

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

Confident sequence learning: A sequence class-label noise filtering technique to improve scene digit recognition

Reading digits from natural images is a challenging computer vision task central to a variety of emerging applications. However, the increased scalability and complexity of datasets or complex applications bring about inevitable label noise. Because the label noise in the scene digit recognition dataset is sequence-like, most existing methods cannot deal with label noise in scene digit recognition. We propose a novel sequence class-label noise filter called Confident Sequence Learning. Confident Sequence Learning consists of two critical parts: the sequence-like confidence segmentation algorithm and the Confident Learning method. The sequence-like confidence segmentation algorithms slice the sequence-like labels and the sequence-like predicted probabilities, reorganize them in the form of the independent stochastic process and the white noise process. The Confident Learning method estimates the joint distribution between observed labels and latent labels using the segmented labels and probabilities. The TRDG dataset and SVHN dataset experiments showed that the confident sequence learning could find label errors with high accuracy and significantly improve the VGG-Attn and the TPS-ResNet-Attn model’s performance in the presence of synthetic sequence class-label noise.

Sequencing-based quantitative mapping of the cellular small RNA landscape

Current next-generation RNA sequencing methods cannot provide accurate quantification of the population of small RNAs within a sample due to strong sequence-dependent biases in capture, ligation, and amplification during library preparation. We report the development of an RNA sequencing method – AQRNA-seq – that minimizes biases and enables absolute quantification of all small RNA species in a sample mixture. Validation of AQRNA-seq library preparation and data mining algorithms using a 963-member microRNA reference library, RNA oligonucleotide standards of varying lengths, and northern blots demonstrated a direct, linear correlation between sequencing read count and RNA abundance. Application of AQRNA-seq to bacterial tRNA pools, a traditionally hard-to-sequence class of RNAs, revealed 80-fold variation in tRNA isoacceptor copy numbers, patterns of site-specific tRNA fragmentation caused by stress, and quantitative maps of ribonucleoside modifications, all in a single AQRNA-seq experiment. AQRNA-seq thus provides a means to quantitatively map the small RNA landscape in cells and tissues.

Satellite DNA landscapes after allotetraploidisation of quinoa (Chenopodium quinoa) reveal unique A and B subgenomes

SUMMARYIf two related plant species hybridise, their genomes are combined within a single nucleus, thereby forming an allotetraploid. How the emerging plant balances two co-evolved genomes is still a matter of ongoing research. Here, we focus on satellite DNA (satDNA), the fastest turn-over sequence class in eukaryotes, aiming to trace its emergence, amplification and loss during plant speciation and allopolyploidisation. As a model, we used Chenopodium quinoa Willd. (quinoa), an allopolyploid crop with 2n=4x=36 chromosomes. Quinoa originated by hybridisation of an unknown female American Chenopodium diploid (AA genome) with an unknown male Old World diploid species (BB genome), dating back 3.3 to 6.3 million years. Applying short read clustering to quinoa (AABB), C. pallidicaule (AA), and C. suecicum (BB) whole genome shotgun sequences, we classified their repetitive fractions, and identified and characterised seven satDNA families, together with the 5S rDNA model repeat. We show unequal satDNA amplification (two families) and exclusive occurrence (four families) in the AA and BB diploids by read mapping as well as Southern, genomic and fluorescent in situ hybridisation. As C. pallidicaule harbours a unique satDNA profile, we are able to exclude it as quinoa’s parental species. Using quinoa long reads and scaffolds, we detected only limited evidence of interlocus homogenisation of satDNA after allopolyploidisation, but were able to exclude dispersal of 5S rRNA genes between subgenomes. Our results exemplify the complex route of tandem repeat evolution through Chenopodium speciation and allopolyploidisation, and may provide sequence targets for the identification of quinoa’s progenitors.

Abordagem CTS e IHC: A importância das cores no design de interfaces de software

This article describes a teaching experience by using STS (Science, Technology and Society) approach in the HCI (Human-Computer Interaction). The experience was carried out in classes about the use of colors in interface design. It aimed at observing students´ perceptions about the use of STS approach in HCI and its impacts on the course of Project of Information Systems. The methodology used in the experiment was to organize the content (colors and STS) within a didactic sequence of six classes. In this sequence, class themes, pedagogical strategies, aims and content were defined. Results showed more interaction between students and lecture in HCI and more students’ interest on using an appropriated code of colors in interface design in the course of Project of Information Systems. It was concluded that engaging methodologies such as STS approach are important and can impact positively for teaching-learning process of students in Software Engineering.

Estimates for the Singular Values of −Δ‎ + q when q is Complex

This chapter considers the Schrödinger operator −Δ‎ + q with q complex. In this case the operator is not self-adjoint and so the analysis of Chapter XI does not apply. It is the distribution of the singular values that is now considered, the technique used being again the localization to cubes forming a covering of Ω‎, together with the Max–Min Principle. Some results are obtained concerning the sequence class lp to which the singular numbers and eigenvalues belong.

Interactive Customer Relationship Management Jasa Infrastruktur Jaringan PT. Meda Cipta Hutama

PT. Meda Cipta Hutama merupakan penyedia jasa IT dan general contractor. Keinginan perusahaan adalah memenuhi kebutuhan pelanggan, di mana diperlukan sebuah sistem informasi yang interaktif. Untuk memenuhi keinginan pelanggan tersebut, dibutuhkan sistem informasi Interactive Customer Relationship Management (i-CRM). I-CRM merupakan suatu usaha untuk menciptakan interaksi antara pelanggan dan perusahaan dengan waktu respon yang tinggi sehingga tidak terjadi kesalahan dalam proses persiapan sampai kepada penyajiannya. Metode pengumpulan data menggunakan metode observasi, wawancara, kuesioner dan studi literatur. Sedangkan metode pengembangan sistem informasinya menggunakan model RAD, yang meliputi UML, dengan diagram use case, sequence class, statechart dan activity. Tujuan pada penelitian ini adalah membangun sistem informasi interactive customer relationship management (i-CRM). Adapun hasil yang diperoleh dari penelitian ini berupa sebuah website i-CRM di mana pihak perusahaan dapat berinteraksi langsung dengan pelanggan dan pelanggan juga dapat dengan mudah melakukan purchase order. Selain itu, i-CRM ini dapat meningkatkan pelayanan yang diberikan yang berdampak pada meningkatnya kepuasan dan loyalitas pelanggan. Sistem ini juga dapat mempermudah perusahaan berinteraksi langsung dengan pelanggan menggunakan fasilitas suggestion dan complain, chatting dan newsletter.