The identification of grain size genes by RapMap reveals directional selection during rice domestication

Cloning quantitative trait locus (QTL) is time consuming and laborious, which hinders the understanding of natural variation and genetic diversity. Here, we introduce RapMap, a method for rapid multi-QTL mapping by employing F2 gradient populations (F2GPs) constructed by minor-phenotypic-difference accessions. The co-segregation standard of the single-locus genetic models ensures simultaneous integration of a three-in-one framework in RapMap i.e. detecting a real QTL, confirming its effect, and obtaining its near-isogenic line-like line (NIL-LL). We demonstrate the feasibility of RapMap by cloning eight rice grain-size genes using 15 F2GPs in three years. These genes explain a total of 75% of grain shape variation. Allele frequency analysis of these genes using a large germplasm collection reveals directional selection of the slender and long grains in indica rice domestication. In addition, major grain-size genes have been strongly selected during rice domestication. We think application of RapMap in crops will accelerate gene discovery and genomic breeding.

Optimal integration of capacitor and PV in distribution network based on nomadic people optimizer

<span>Since the last decades, capacitor and photovoltaics (PV) are installed in distribution networks to meet the increasing in system loads. In this paper, a new application of nomadic people optimizer (NPO) algorithm is proposed to obtain the best locations and sizes of capacitor and PV alone or simultaneously in radial distribution system (RDS). Also, reactive loss sensitivity factor (QLSF) can be used for obtaining the candidate locations for installing PV and capacitor units in RDS. The efficiency of the presented technique can be applied on IEEE 69-bus and IEEE 33-bus RDS. From simulation result, installing capacitor and PV units alone in RDS decreases the total losses and increases the bus voltages. Also, simultaneous integration of PV and capacitor units give better results than integration capacitor and PV units alone in distribution network. The presented algorithm is able to explore most area of search and obtain better results than recent optimizations algorithms.</span>

Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

Consensus clustering applied to multi-omics disease subtyping

Background Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. Conclusion We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics.

Intelligent Sensors for Real-Time Decision-Making

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.

Programmable and reprocessable multifunctional elastomeric sheets for soft origami robots

Tunable, soft, and multifunctional robots are contributing to developments in medical and rehabilitative robotics, human-machine interaction, and intelligent home technology. A key aspect of soft robot fabrication is the ability to use flexible and efficient schemes to enable the seamless and simultaneous integration of configurable structures. Here, we report a strategy for programming design features and functions in elastomeric surfaces. We selectively modified these elastomeric surfaces via laser scanning and then penetrated them with an active particle–infused solvent to enable controllable deformation, folding, and functionality integration. The functionality of the elastomers can be erased by a solvent retreatment and reprocessed by repeating the active particle infusion process. We established a platform technique for fabricating programmable and reprocessable elastomeric sheets by varying detailed morphology patterns and active particles. We used this technique to produce functional soft ferromagnetic origami robots with seamlessly integrated structures and various active functions, such as robots that mimic flowers with petals bent at different angles and with different curvatures, low-friction swimming robots, multimode locomotion carriers with gradient-stiffness claws for protecting and delivering objects, and frog-like robots with adaptive switchable coloration that responds to external thermal and optical stimuli.

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.

Development and Validation of a Program Logic Model (PLM) to Support "Near Miss Mother"(NMM): A Nominal Group Technique (NGT)

Background: Mothers who have experienced a near miss event, their normal life is affected by physical, psychological, emotional, social and economic adverse effects. The aim of this study is to develop a supportive program for near miss mothers (NMM), based on a program logical model (PLM) that has been validated using the nominal group technique (NGT).Methods: After conducting qualitative and systematic reviews studies to assess the needs, components of PLM were extracted that provided the framework for the utilization of activities, outputs, outcomes and impact. A Nominal Group Technique method done in a one-day workshop with the participation of 12 professionals was held in November 2020.Results: Eight strategies used in draft support programs based on the logical model, included the following: "psychological", "fertility / childbearing", "information", "care quality improvement", "socio-cultural", "financial", "breastfeeding" and "nutritional". The validation of the program was done based on the five steps of the NGT during the steps of creating ideas, silent generation of ideas, round robin, clarification of ideas, Prioritization. Finally, a final program was presented to support NMM.Conclusions: Simultaneous integration in the PLM and NGT method allowed the first program developed to support NMM to be comprehensive and complete. Using this evidence-based program can help reduce the burden of maternal morbidities in millions of women around the world and prevent long-term complications and shorten their rehabilitation phase.