Minding the Gaps in a Video Action Analysis Pipeline

2019 ◽  
Author(s):  
Jia Chen ◽  
Jiang Liu ◽  
Junwei Liang ◽  
Ting-Yao Hu ◽  
Wei Ke ◽  
...  
Keyword(s):  
Analysis Pipeline ◽  
Action Analysis

MENINGKATKAN PRESTASI BELAJAR IPS MELALUI MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW PADA SISWA KELAS VI SD NEGERI 020 TEMBILAHAN HILIR

2018 ◽  
Vol 2 (1) ◽  
pp. 40
Author(s):  
St. Hadijah
Keyword(s):  
Cooperative Learning ◽  
Learning Outcomes ◽  
Positive Impact ◽  
Positive Influence ◽  
Learning Motivation ◽  
Research Activity ◽  
Learning Achievement ◽  
Action Analysis

The study aims to explain the effect of Jigsaw type cooperative learning on the IPS learning outcomes and howfar the comprehension and mastery of IPS subjects after the implementation of Jigsaw type cooperative learningon the students of class VI of SD Negeri 020 Tembilahan Hilir. This research activity was conducted at SDNegeri 020 Tembilahan Hilir. This research was conducted in October odd semester of academic year2016/2017 with subject of 20 students. The study was conducted in two cycles with qualitative descriptivetechnique. The results of the action analysis show that: First, Jigsaw type cooperative learning has a positiveimpact in improving students 'learning achievement marked by the improvement of students' learning mastery inevery cycle, that is cycle I (60.00%) and cycle II (90.00%). Second, the application of cooperative learning typeJigsaw has a positive influence, which can improve students' learning motivation in IPS learning, it is shown byenthusiastic students who stated that students are interested and interested in cooperative learning type Jigsawso they become motivated to learn. Third, Jigsaw type cooperative learning has a positive impact on cooperationamong students, it is shown that there is a responsibility in groups where students are better able to teach theirless fortunate friends.

scholarly journals A novel stepwise integrative analysis pipeline reveals distinct microbiota-host interactions and link to symptoms in irritable bowel syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annikka Polster ◽  
Lena Öhman ◽  
Julien Tap ◽  
Muriel Derrien ◽  
Boris Le Nevé ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Complex Variable ◽  
16S Rrna Gene Sequencing ◽  
Integrative Analysis ◽  
Rrna Gene ◽  
Analysis Pipeline ◽  
Host Interactions ◽  
Functional Relationships ◽  
Irritable Bowel ◽  
Host Genes

AbstractAlthough incompletely understood, microbiota-host interactions are assumed to be altered in irritable bowel syndrome (IBS). We, therefore, aimed to develop a novel analysis pipeline tailored for the integrative analysis of microbiota-host interactions and association to symptoms and prove its utility in a pilot cohort. A multilayer stepwise integrative analysis pipeline was developed to visualize complex variable associations. Application of the pipeline was demonstrated on a dataset of IBS patients and healthy controls (HC), using the R software package to analyze colonic host mRNA and mucosal microbiota (16S rRNA gene sequencing), as well as gastrointestinal (GI) and psychological symptoms. In total, 42 IBS patients (57% female, mean age 33.6 (range 18–58)) and 20 HC (60% female, mean age 26.8 (range 23–41)) were included. Only in IBS patients, mRNA expression of Toll-like receptor 4 and genes associated with barrier function (PAR2, OCLN, TJP1) intercorrelated closely, suggesting potential functional relationships. This host genes-based “permeability cluster” was associated to mucosa-adjacent Chlamydiae and Lentisphaerae, and furthermore associated to satiety as well as to anxiety, depression and fatigue. In both IBS patients and HC, chromogranins, secretogranins and TLRs clustered together. In IBS patients, this host genes-based “immune-enteroendocrine cluster” was associated to specific members of Firmicutes, and to depression and fatigue, whereas in HC no significant association to microbiota was identified. We have developed a stepwise integrative analysis pipeline that allowed identification of unique host-microbiota intercorrelation patterns and association to symptoms in IBS patients. This analysis pipeline may aid in advancing the understanding of complex variable associations in health and disease.

scholarly journals Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs

tppj ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jenna Hershberger ◽  
Nicolas Morales ◽  
Christiano C. Simoes ◽  
Bryan Ellerbrock ◽  
Guillaume Bauchet ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Spectral Data ◽  
Data Storage ◽  
Analysis Pipeline ◽  
Breeding Programs

scholarly journals Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 840
Author(s):  
Willem Q. M. van de Koot ◽  
Larissa J. J. van Vliet ◽  
Weilun Chen ◽  
John H. Doonan ◽  
Candida Nibau
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Plant Density ◽  
Important Determinant ◽  
Carbon Sink ◽  
High Accuracy ◽  
Analysis Pipeline ◽  
Colony Density ◽  
Water Table Management ◽  
And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

scholarly journals Machine learning-based classification of mitochondrial morphology in primary neurons and brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Garrett M. Fogo ◽  
Anthony R. Anzell ◽  
Kathleen J. Maheras ◽  
Sarita Raghunayakula ◽  
Joseph M. Wider ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cortical Neurons ◽  
Mitochondrial Dynamics ◽  
Automated Image Analysis ◽  
Mitochondrial Morphology ◽  
Analysis Pipeline ◽  
Genetic Ablation ◽  
Block Face

AbstractThe mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

scholarly journals Monitoring the microbiome for food safety and quality using deep shotgun sequencing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kristen L. Beck ◽  
Niina Haiminen ◽  
David Chambliss ◽  
Stefan Edlund ◽  
Mark Kunitomi ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Environmental Changes ◽  
Pet Food ◽  
Analysis Pipeline ◽  
Multiple Sequence ◽  
Food Ingredients ◽  
Total Rna ◽  
Specific Species ◽  
Ingredient Composition ◽  
Food Safety And Quality

AbstractIn this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

scholarly journals xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery

2019 ◽  
Vol 24 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Raimo Franke ◽  
Bettina Hinkelmann ◽  
Verena Fetz ◽  
Theresia Stradal ◽  
Florenz Sasse ◽  
...  
Keyword(s):  
Data Analysis ◽  
Bioactive Compounds ◽  
Mode Of Action ◽  
Mammalian Cells ◽  
Cellular Response ◽  
Label Free ◽  
Synthesis Inhibitor ◽  
Analysis Pipeline ◽  
Bioactive Natural Products ◽  
Data Analysis Pipeline

Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.

Sign in / Sign up

Export Citation Format

Share Document