Showing 561 - 580 results of 3,583 for search '((\ sources detection functions\ ) OR (( (resources OR sources) OR source) selection function\ ))', query time: 0.42s Refine Results
  1. 561
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    Harnessing polydiacetylene (PDA): A review of structural mechanics and infectious disease detection by Soumendu Patra, Harshita Shand, Sayan Ghosal, Suvankar Ghorai

    Published 2025-07-01
    “…These biosensors can also be functionalized with particular capture agents, such as antibodies or nucleic acid probes, to selectively bind and detect the target analytes. …”
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    Genome-Wide Association Analysis and Genomic Selection for Growth Traits in Grass Carp (<i>Ctenopharyngodon idella</i>) by Yuxuan Chen, Qiaozhen Yu, Wenyao Lv, Tao Sheng, Lang Gui, Junqiang Qiu, Xiaoyan Xu, Jiale Li

    Published 2025-06-01
    “…This study provides the first comprehensive SNP resource for grass carp growth traits with different dietary treatments, bridging GWAS and genomic prediction to accelerate marker-assisted selection. …”
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    Article
  5. 565

    Venturing Into the Unknown: The Importance of Variable Selection When Modelling Alien Species Under Non‐Analogue Climatic Conditions by Tom Vorstenbosch, Franz Essl, Bernd Lenzner, Johannes Wessely, Stefan Dullinger

    Published 2024-10-01
    “…Here, we tested the assumption that this aspect of model design is a major source of uncertainty, especially when projections are made to non‐analogue climates. …”
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    New groups of highly divergent proteins in families as old as cellular life with important biological functions in the ocean by Duncan Sussfeld, Romain Lannes, Eduardo Corel, Guillaume Bernard, Pierre Martin, Eric Bapteste, Eric Pelletier, Philippe Lopez

    Published 2025-06-01
    “…Results Here, we used an iterative, network-based approach for remote homology detection, to probe a dataset of 40 million ORFs predicted in marine environments. …”
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    Raw-Data Driven Functional Data Analysis with Multi-Adaptive Functional Neural Networks for Ergonomic Risk Classification Using Facial and Bio-Signal Time-Series Data by Suyeon Kim, Afrooz Shakeri, Seyed Shayan Darabi, Eunsik Kim, Kyongwon Kim

    Published 2025-07-01
    “…Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. …”
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  11. 571

    ChiMSource improves the accuracy of studies on novel amino acid sequences by predicting alternative sources of mass spectrometry-derived peptides by Umut Çakır, Noujoud Gabed, Ali Yurtseven, Igor Kryvoruchko

    Published 2025-01-01
    “…Here, we present a highly flexible program that predicts alternative chimeric and non-chimeric sources of peptides detected by MS proteomics.…”
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    Highly Efficient Biphoton Generation from Thin Dense Atomic Ensemble by Heewoo Kim, Hansol Jeong, Han Seb Moon

    Published 2025-04-01
    “…Strongly correlated bright biphotons are generated via spontaneous four‐wave mixing from a dense atomic ensemble based on the 6S1/2–6P3/2–6D5/2 transition of 133Cs. Biphoton source achieves a detected biphoton count rate of 100 kilo‐counts per second, a heralding efficiency of 15%, and a maximum normalized crosscorrelation function value of 100 between the signal and idler photons, despite the low detector efficiency of a silicon avalanche photodetector being less than 25% at 917 nm. …”
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  14. 574

    Phage display of cDNA libraries: enrichment of cDNA expression using open reading frame selection by Peggy Ho Faix, Michael A. Burg, Michelle Gonzales, Edward P. Ravey, Andrew Baird, David Larocca

    Published 2004-06-01
    “…The high level of cDNA expression obtained by ORF selection suggests that ORF-enriched phage cDNA libraries prepared by these methods will be useful as functional genomics tools for identifying natural ligands from various source tissues.…”
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    A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. by George Nicholson, Mattias Rantalainen, Jia V Li, Anthony D Maher, Daniel Malmodin, Kourosh R Ahmadi, Johan H Faber, Amy Barrett, Josine L Min, N William Rayner, Henrik Toft, Maria Krestyaninova, Juris Viksna, Sudeshna Guha Neogi, Marc-Emmanuel Dumas, Ugis Sarkans, MolPAGE Consortium, Peter Donnelly, Thomas Illig, Jerzy Adamski, Karsten Suhre, Maxine Allen, Krina T Zondervan, Tim D Spector, Jeremy K Nicholson, John C Lindon, Dorrit Baunsgaard, Elaine Holmes, Mark I McCarthy, Chris C Holmes

    Published 2011-09-01
    “…Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. …”
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  17. 577

    Metal and metal oxide nanomaterials for heavy metal remediation: novel approaches for selective, regenerative, and scalable water treatment by David B. Olawade, David B. Olawade, David B. Olawade, Ojima Z. Wada, Bamise I. Egbewole, Oluwaseun Fapohunda, Abimbola O. Ige, Sunday Oluwadamilola Usman, Olawale Ajisafe

    Published 2024-10-01
    “…The review identifies several promising nanomaterials, such as graphene oxide, carbon nanotubes, and metal-organic frameworks, which exhibit high surface areas, tunable surface chemistries, and excellent adsorption capacities. Surface functionalization with specific functional groups (e.g., carboxyl, amino, thiol) significantly enhances the selectivity for target heavy metal ions. …”
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    Ti3C2Tx@Pt nanocomposite based colorimetric and electrochemical dual-mode platform for glucose detection by Xuming Sun, Mingyan Zhang, Jinlong Chang, Linyan Xie, Qiongqiong Ren, Wenshuai Jiang, Zhen Jin, Shiqi Pan, Xiuli Yang, Wu Ren

    Published 2025-06-01
    “…Glucose is the main source of energy in cellular metabolism and is essential for normal cellular function and growth. …”
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  19. 579

    YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

    Published 2025-01-01
    “…We apply the YOLOX algorithm as the basic framework and improve it by selecting the optimal optimizer, activation function, attention mechanism, and loss function. …”
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