325 research outputs found
Development of a genome-wide multiple duplex-SSR protocol and its applications for the identification of selfed progeny in switchgrass
Background: Switchgrass (Panicum virgatum) is a herbaceous crop for the cellulosic biofuel feedstock development in the USA and Europe. As switchgrass is a naturally outcrossing species, accurate identification of selfed progeny is important to producing inbreds, which can be used in the production of heterotic hybrids. Development of a technically reliable, time-saving and easily used marker system is needed to quantify and characterize breeding origin of progeny plants of targeted parents.Results: Genome-wide screening of 915 mapped microsatellite (simple sequence repeat, SSR) markers was conducted, and 842 (92.0%) produced clear and scorable bands on a pooled DNA sample of eight switchgrass varieties. A total of 166 primer pairs were selected on the basis of their relatively even distribution in switchgrass genome and PCR amplification quality on 16 tetraploid genotypes. Mean polymorphic information content value for the 166 markers was 0.810 ranging from 0.116 to 0.959. From them, a core set of 48 loci, which had been mapped on 17 linkage groups, was further tested and optimized to develop 24 sets of duplex markers. Most of (up to 87.5%) targeted, but non-allelic amplicons within each duplex were separated by more than 10-bp. Using the established duplex PCR protocol, selfing ratio (i.e., selfed/all progeny x100%) was identified as 0% for a randomly selected open-pollinated 'Kanlow' genotype grown in the field, 15.4% for 22 field-grown plants of bagged inflorescences, and 77.3% for a selected plant grown in a growth chamber.Conclusions: The study developed a duplex SSR-based PCR protocol consisting of 48 markers, providing ample choices of non-tightly-linked loci in switchgrass whole genome, and representing a powerful, time-saving and easily used method for the identification of selfed progeny in switchgrass. The protocol should be a valuable tool in switchgrass breeding efforts.Peer reviewedPlant and Soil Science
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
Surgical action triplet recognition provides a better understanding of the
surgical scene. This task is of high relevance as it provides to the surgeon
with context-aware support and safety. The current go-to strategy for improving
performance is the development of new network mechanisms. However, the
performance of current state-of-the-art techniques is substantially lower than
other surgical tasks. Why is this happening? This is the question that we
address in this work. We present the first study to understand the failure of
existing deep learning models through the lens of robustness and explainabilty.
Firstly, we study current existing models under weak and strong
perturbations via adversarial optimisation scheme. We then provide the
failure modes via feature based explanations. Our study revels that the key for
improving performance and increasing reliability is in the core and spurious
attributes. Our work opens the door to more trustworthiness and reliability
deep learning models in surgical science
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
Few-shot semantic segmentation (FSS) aims to achieve novel objects
segmentation with only a few annotated samples and has made great progress
recently. Most of the existing FSS models focus on the feature matching between
support and query to tackle FSS. However, the appearance variations between
objects from the same category could be extremely large, leading to unreliable
feature matching and query mask prediction. To this end, we propose a
Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate
latent context structure in query images. Specifically, we propose a
Support-induced Graph Reasoning (SiGR) module to capture salient query object
parts at different semantic levels with a Support-induced GCN. Furthermore, an
instance association (IA) module is designed to capture high-order instance
context from both support and query instances. By integrating the proposed two
modules, SiGCN can learn rich query context representation, and thus being more
robust to appearance variations. Extensive experiments on PASCAL-5i and
COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.Comment: Accepted in BMVC2022 as oral presentatio
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
One of the ultimate goals of representation learning is to achieve
compactness within a class and well-separability between classes. Many
outstanding metric-based and prototype-based methods following the
Expectation-Maximization paradigm, have been proposed for this objective.
However, they inevitably introduce biases into the learning process,
particularly with long-tail distributed training data. In this paper, we reveal
that the class prototype is not necessarily to be derived from training
features and propose a novel perspective to use pre-defined class anchors
serving as feature centroid to unidirectionally guide feature learning.
However, the pre-defined anchors may have a large semantic distance from the
pixel features, which prevents them from being directly applied. To address
this issue and generate feature centroid independent from feature learning, a
simple yet effective Semantic Anchor Regularization (SAR) is proposed. SAR
ensures the interclass separability of semantic anchors in the semantic space
by employing a classifier-aware auxiliary cross-entropy loss during training
via disentanglement learning. By pulling the learned features to these semantic
anchors, several advantages can be attained: 1) the intra-class compactness and
naturally inter-class separability, 2) induced bias or errors from feature
learning can be avoided, and 3) robustness to the long-tailed problem. The
proposed SAR can be used in a plug-and-play manner in the existing models.
Extensive experiments demonstrate that the SAR performs better than previous
sophisticated prototype-based methods. The implementation is available at
https://github.com/geyanqi/SAR.Comment: AAAI 202
Dual-Enhanced Photocatalytic Activity of Fe-Deposited Titanate Nanotubes Used for Simultaneous Removal of As(III) and As(V)
Fe-deposited
titanate nanotubes (Fe-TNTs) with high photocatalytic
activity and adsorptive performance were synthesized through a one-step
hydrothermal method. Initial AsÂ(III) oxidation followed by AsÂ(V) adsorption
by Fe-TNTs could simultaneously remove these two toxic pollutants
from aqueous solutions. The apparent rate constant value for photo-oxidation
of AsÂ(III) under UV irradiation by Fe-TNTs was almost 250 times that
of unmoidified TNTs. Under visible light, the Fe-TNTs also exhibited
enhanced photocatalytic activity after Fe was deposited. Fe<sup>3+</sup> located in the interlayers of TNTs acted as temporary electron-
or hole-trapping sites, and attached α-Fe<sub>2</sub>O<sub>3</sub> played the role of a charge carrier for electrons transferred from
TNTs. These two effects inhibited electron–hole pair recombination
thus promoting photocatalysis. Moreover, the AsÂ(V) adsorptive performance
of Fe-TNTs also improved, owing to the presence of additional adsorption
sites, α-Fe<sub>2</sub>O<sub>3</sub>, as well as increased pH<sub>PZC</sub>. Furthermore, Fe-TNTs exhibited good photocatalytic and
adsorptive performace even after 5 reuse cycles. The present tests,
concerning an initial AsÂ(III) photocatalysis and subsequent AsÂ(V)
adsorption process, highlight the feasibility and importance of Fe
used to modify TNTs. This study proposes a feasible method to simultaneously
remove AsÂ(III) and AsÂ(V) from contaminated water using a novel Ti-based
nanomaterial
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich task
before being fine-tuned on a downstream task, has emerged as a powerful
technique in natural language processing (NLP). The effectiveness of transfer
learning has given rise to a diversity of approaches, methodology, and
practice. In this paper, we explore the landscape of transfer learning
techniques for NLP by introducing a unified framework that converts all
text-based language problems into a text-to-text format. Our systematic study
compares pre-training objectives, architectures, unlabeled data sets, transfer
approaches, and other factors on dozens of language understanding tasks. By
combining the insights from our exploration with scale and our new ``Colossal
Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks
covering summarization, question answering, text classification, and more. To
facilitate future work on transfer learning for NLP, we release our data set,
pre-trained models, and code.Comment: Final version as published in JML
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering
immense potential for enhancing traffic monitoring accuracy and promoting road
safety measures through the utilisation of advanced machine learning
algorithms. However, existing datasets for multi-object tracking in traffic
videos often feature limited instances or focus on single classes, which cannot
well simulate the challenges encountered in complex traffic scenarios. To
address this gap, we introduce TrafficMOT, an extensive dataset designed to
encompass diverse traffic situations with complex scenarios. To validate the
complexity and challenges presented by TrafficMOT, we conducted comprehensive
empirical studies using three different settings: fully-supervised,
semi-supervised, and a recent powerful zero-shot foundation model Tracking
Anything Model (TAM). The experimental results highlight the inherent
complexity of this dataset, emphasising its value in driving advancements in
the field of traffic monitoring and multi-object tracking.Comment: 17 pages, 7 figure
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