Target Template Research Articles

Can templates-for-rejection suppress real-world affective objects in visual search?

Previous evidence has suggested that feature-based templates-for-rejection can be maintained in working memory to suppress matching features in the environment. Currently, this effect has only been demonstrated using abstract neutral shapes, meaning that it is unclear whether this generalizes to real-world images, including aversive stimuli. In the current investigation, participants searched amongst an array of real-world objects for a target, after being precued with either a distractor template, target template, or a no template baseline. In Experiment 1, where both distractor and target template cues were presented randomly on a trial-by-trial basis, there was moderate evidence of increased capture by aversive distractors after the distractor template cue. In Experiment 2a, however, when distractor templates were the only available cue and more time was given to encode the cue features, there was moderate evidence of effective distractor inhibition for real-world aversive and neutral stimuli. In Experiment 2b, when the task required a slower more effortful comparison of target features to stereotypical object representations, there was weaker evidence of inhibition, though there was still modest evidence suggesting effective inhibition of aversive distractors. A Bayesian meta-analysis revealed that across Experiment 2, aversive distractors showed strong cumulative evidence of effective inhibition, but inconsistent inhibition for neutral distractors. The results are interpreted from a rational search behaviour framework, which suggests that individuals utilize informative cues when they enable the most beneficial strategy and are accessible, and apply these to distractors when they cause sufficient disruption, either to search speed or emotional state.

Task-adaptive changes to the target template in response to distractor context: Separability versus similarity.

Theories of attention hypothesize the existence of an attentional template that contains target features in working or long-term memory. It is frequently assumed that the template contains a veridical copy of the target, but recent studies suggest that this is not true when the distractors are linearly separable from the target. In such cases, target representations shift "off-veridical" in response to the distractor context, presumably because doing so is adaptive and increases the representational distinctiveness of targets from distractors. However, some have argued that the shifts may be entirely explained by perceptual biases created by simultaneous color contrast. Here we address this debate and test the more general hypothesis that the target template is adaptively shaped by elements of the distractor context needed to distinguish targets from distractors. We used a two-dimensional target and separately manipulated the linear separability of one dimension (color) and the visual similarity of the other (orientation). We found that target shifting along the linearly separable color dimension was dependent on the similarity of targets-to-distractors along the other dimension. The target representations were consistent with a postexperiment strategy questionnaire in which participants reported using color more when orientation was hard to use, and orientation more when it was easier to use. We conclude that the target template is task-adaptive and exploit features in the distractor context that most predictably distinguish targets from distractors to increase visual search efficiency. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

UnifiedTT: Visual tracking with unified transformer

Target tracking is an important research task in computer vision. Existing tracking algorithms based on Siamese networks often suffer from the problem of information redundancy between adjacent frames and a lack of ability to capture global dependencies. When similar backgrounds appear around the target, the tracking performance usually significantly decreases. Although target tracking algorithms based on deep convolution and the Transformer have partially addressed these issues, achieving a good balance between the two remains a challenge. In this work, we propose a unified convolution and self-attention Siamese network for target tracking. By utilizing a feature extraction backbone network based on integrated convolution and self-attention styles, we are able to capture globally important regions and key frames while greatly reducing local redundant computations, thereby improving tracking performance. We apply this approach to the task of target tracking and enhance the feature extraction capabilities of both the target template and search template. Experimental results show that our proposed tracking algorithm outperforms some recent classical tracking algorithms, especially achieving improvements of 10.7 % on the high-diversity dataset GOT-10K and 24.7 % on the large-scale and high-quality LaSOT dataset.

The Preparatory Activation of Guidance Templates for Visual Search and of Target Templates in Non-Search Tasks.

Representations of task-relevant object attributes (attentional templates) control the adaptive selectivity of visual processing. Previous studies have demonstrated that templates involved in the guidance of attention during visual search are activated in a preparatory fashion prior to the arrival of visual search displays. The current study investigated whether such proactive mechanisms are also triggered in non-search tasks, where attentional templates do not mediate the guidance of attention towards targets amongst distractors but are still necessary for subsequent target recognition processes. Participants either searched for colour-defined targets among multiple distractors or performed two other non-search tasks where imperative stimuli appeared without competing distractors (a colour-based Go/NoGo task, and a shape discrimination task where target colour was constant and could thus be ignored). Preparatory activation of colour-selective templates was tracked by measuring N2pc components (markers of attention allocation) to task-irrelevant colour singleton probes flashed every 200 ms during the interval between target displays. As expected, N2pcs were triggered by target-coloured probes in the search task, indicating that a corresponding guidance template was triggered proactively. Critically, clear probe N2pcs were also observed in the Go/NoGo task, and even in the shape discrimination task in an attenuated fashion. These findings demonstrate that the preparatory activation of feature-selective attentional task settings is not uniquely associated with the guidance of visual search but is also present in other types of visual selection tasks where guidance is not required.

Single-Cell Imaging of mRNA by Target RNA-Initiated RCA.

We present a powerful method for direct mRNA detection based on ligation-based recognition and in situ amplification, capable of single-cell imaging mRNA at single-nucleotide and single-molecule resolution. Attributed to the use of Splint R ligase that can ligate padlock probe with RNA as target template, this method can efficiently detect mRNA in the absence of reverse transcription. This method enables spatial localization and correlation analysis of gene expression in single cells, which helps us to elucidate gene function and regulatory mechanisms.

Magnetic nanoparticle-facilitated rapid mass production of high affinity polymeric materials (nanoMIPs) for protein recognition and biosensing.

Molecularly imprinted polymers (MIPs) have been investigated extensively for broad applications in diagnostics, imaging and therapeutics due to their antibody-like specificity, high stability, and low-cost and rapid production when compared with biological antibodies. Yet, their wide-scale adoption and commercial viability are limited due to low yields and relatively lengthy preparations of current methods. We report the novel application of protein-functionalised magnetic nanoparticles (MNPs) to enable the rapid mass production of nanoMIPs for protein recognition. An aldehyde-functionalised MNP (MNP@CHO) precursor was synthesised using a one-pot microwave method in less than 20 minutes, resulting in 330 mg yield for a 30 mL reaction volume. The MNP@CHO precursor (10 mg) was subsequently functionalised with 600 μg of a target template protein, giving MNP@protein. In the presence of an N-hydroxymethylacrylamide (NHMA) functional monomer and N,N'-methylene bisacrylamide as a crosslinker, the MNP@protein particles served as nucleants for the mass production of nanoMIPs in a 20-30 minute synthesis process. Subsequently, the nanoMIPs could be harvested with sonication and then retrieved using a magnet, leaving the MNP@protein particles to be recycled and re-used at least 5 times for further nanoMIP production cycles. In general, 10 mg of MNP@protein produced 10 mg of nanoMIP with a 20% decrease in the yield over the 5 synthesis cycles. For the bovine haemoglobin nanoMIP, the KD was determined to be 3.47 × 10-11 M, a binding affinity rivalling values found for monoclonal antibodies. We also demonstrate that the methodology is generic by producing high-affinity nanoMIPs for other proteins including albumin, lysozyme and SARS-CoV-2 recombinant protein. We therefore present a facile route to produce nanoMIPs in large industrially relevant quantities (hundreds of mg) and at short timescales (within a day). Our method offers realistic opportunities for the industry to adopt such materials as an antibody replacement technology in diagnostics, biological extraction and therapeutics.

A Hypothetical Approach to Concentrate Microorganisms from Human Urine Samples Using Paper-Based Adsorbents for Point-of-Care Molecular Assays.

This hypothesis demonstrates that the efficiency of loop-mediated isothermal amplification (LAMP) for nucleic acid detection can be positively influenced by the preconcentration of microbial cells onto hydrophobic paper surfaces. The mechanism of this model is based on the high affinity of microbes towards hydrophobic surfaces. Extensive studies have demonstrated that hydrophobic surfaces exhibit enhanced bacterial and fungal adhesion. By exploiting this inherent affinity of hydrophobic paper substrates, the preconcentration approach enables the adherence of a greater number of target cells, resulting in a higher concentration of target templates for amplification directly from urine samples. In contrast to conventional methods, which often involve complex procedures, this approach offers a simpler, cost-effective, and user-friendly alternative. Moreover, the integration of cell adhesion, LAMP amplification, and signal readout within paper origami-based devices can provide a portable, robust, and highly efficient platform for rapid nucleic acid detection. This innovative hypothesis holds significant potential for point-of-care (POC) diagnostics and field surveillance applications. Further research and development in this field will advance the implementation of this technology, contributing to improved healthcare systems and public health outcomes.

Sparse regularized correlation filter for UAV object tracking with adaptive contextual learning and keyfilter selection

Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e., boundary effect and filter corruption. To handle them, in this work, we propose a novel ℓ1 regularization correlation filter with adaptive contextual learning and keyfilter selection for UAV tracking. Firstly, we adaptively detect the positions of effective contextual distractors by the aid of the distribution of local maximum values on the response map of current frame which is generated by using the previous correlation filter model. Next, we eliminate inconsistent labels for the tracked target by removing one on each distractor and develop a new score scheme for each distractor. Then, we can select the keyfilter from the filters pool by finding the maximal similarity between the target at the current frame and the target template corresponding to each filter in the filters pool. Finally, quantitative and qualitative experiments on three authoritative UAV datasets show that the proposed method is superior to the state-of-the-art tracking methods based on correlation filter framework.

IoUformer: Pseudo-IoU prediction with transformer for visual tracking

Siamese tracking has witnessed tremendous progress in tracking paradigm. However, its default box estimation pipeline still faces a crucial inconsistency issue, namely, the bounding box decided by its classification score is not always best overlapped with the ground truth, thus harming performance. To this end, we explore a novel simple tracking paradigm based on the intersection over union (IoU) value prediction. To first bypass this inconsistency issue, we propose a concise target state predictor termed IoUformer, which instead of default box estimation pipeline directly predicts the IoU values related to tracking performance metrics. In detail, it extends the long-range dependency modeling ability of transformer to jointly grasp target-aware interactions between target template and search region, and search sub-region interactions, thus neatly unifying global semantic interaction and target state prediction. Thanks to this joint strength, IoUformer can predict reliable IoU values near-linear with the ground truth, which paves a safe way for our new IoU-based siamese tracking paradigm. Since it is non-trivial to explore this paradigm with pleased efficacy and portability, we offer the respective network components and two alternative localization ways. Experimental results show that our IoUformer-based tracker achieves promising results with less training data. For its applicability, it still serves as a refinement module to consistently boost existing advanced trackers.

A sensitive Raman spectroscopy sensor for determination cardiac troponin I based on proteolytic peptide magnetic imprinting technology

This paper proposes a selective strategy for quantitative detecting cardiac troponin I (cTnI) in clinical serum samples by using Raman spectroscopy combined with magnetic molecularly imprinted polymers (MMIPs). The signature peptide was selected as a template for molecular imprinting due to its chemical stability and small molecular mass. MMIPs were synthetized with the signature peptide as template and methylacrylic acid as monomer in presence of vinyl-modified magnetic particles. MMIPs with high magnetic character and adsorption properties were able to be stained by Coomassie Brilliant Blue G-250 after the target template was specifically captured, which showing a characteristic absorption peak at Raman shift 1610 cm−1. The Raman signal intensity at 1610 cm−1 showed a linear relationship in the 0.01 to 100 ng·mL−1 concentration range. The recovery of cTnI in serum were 92.7–111.2 % with relative standard deviation in the range of 2.51 % to 8.52 %. This paper has important practical application for preliminary rapid detection of cTnI in clinical serum samples.

Supplemental Material for Task-Adaptive Changes to the Target Template in Response to Distractor Context: Separability Versus Similarity

Supplemental Material for Task-Adaptive Changes to the Target Template in Response to Distractor Context: Separability Versus Similarity

A new multiplexed magnetic capture-Droplet digital PCR tool for monitoring wildlife population health and pathogen surveillance.

Anthropogenic stressors are exacerbating the emergence and spread of pathogens worldwide. In regions like the Arctic, where ecosystems are particularly susceptible, marked changes are predicted in regional diversity, intensity, and patterns of infectious diseases. To understand such rapidly changing host-pathogen dynamics and mitigate the impacts of novel pathogens, we need sensitive disease surveillance tools. We developed and validated a novel multiplexed, magnetic capture, and ddPCR tool for the surveillance of multiple pathogens in polar bears, a sentinel species that is considered susceptible to climate change and other stressors with a pan-Arctic distribution. Through sequence-specific magnetic capture, we concentrated five target template sequences from three zoonotic bacteria (Erysipelothrix rhusiopathiae, Francisella tularensis, and Mycobacterium tuberculosis complex) and two parasitic (Toxoplasma gondii and Trichinella spp.) pathogens from large quantities (<100 g) of host tissue. We then designed and validated two multiplexed probe-based ddPCR assays for the amplification and detection of the low-concentration target DNA. Validations used 48 polar bear tissues (muscle and liver). We detected 14, 1, 3, 4, and 22 tissue positives for E. rhusiopathiae, F. tularensis, M. tuberculosis complex, T. gondii, and Trichinella spp., respectively. These multiplexed assays offer a rapid, specific tool for quantifying and monitoring the changing geographical and host distributions of pathogens relevant to human and animal health.

A Mongolian-Chinese Neural Machine Translation Model Based on Soft Target Templates and Contextual Knowledge

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers.

Learned associations serve as target proxies during difficult but not easy visual search

The target template contains information in memory that is used to guide attention during visual search and is typically thought of as containing features of the actual target object. However, when targets are hard to find, it is advantageous to use other information in the visual environment that is predictive of the target's location to help guide attention. The purpose of these studies was to test if newly learned associations between face and scene category images lead observers to use scene information as a proxy for the face target. Our results showed that scene information was used as a proxy for the target to guide attention but only when the target face was difficult to discriminate from the distractor face; when the faces were easy to distinguish, attention was no longer guided by the scene unless the scene was presented earlier. The results suggest that attention is flexibly guided by both target features as well as features of objects that are predictive of the target location. The degree to which each contributes to guiding attention depends on the efficiency with which that information can be used to decode the location of the target in the current moment. The results contribute to the view that attentional guidance is highly flexible in its use of information to rapidly locate the target.

In vivo CRISPR/LbCas12a-mediated knock-in and knock-out in Atlantic salmon (Salmo salar L.)

Genome editing using the CRISPR/Cas system offers the potential to enhance current breeding programs and introduce desirable genetic traits, including disease resistance, in salmon aquaculture. Several nucleases are available using this system, displaying differences regarding structure, cleavage, and PAM requirement. Cas9 is well established in Atlantic salmon, but Cas12a has yet to be tested in vivo in this species. In the present work, we microinjected salmon embryos with LbCas12a ribonucleoprotein complexes targeting the pigmentation gene solute carrier family 45 member 2 (slc45a2). Using CRISPR/LbCas12a, we were able to knock-out slc45a2 and knock-in a FLAG sequence element by providing single-stranded DNA templates. High-throughput sequencing revealed perfect HDR rates up to 34.3% and 54.9% in individual larvae using either target or non-target strand template design, respectively. In this work, we demonstrate the in vivo application of CRISPR/LbCas12a in Atlantic salmon, expanding the toolbox for editing the genome of this important aquaculture species.

Universal primer multiplex PCR assay for detection and genotyping of porcine astroviruses

Porcine astroviruses (PAstV) are members of the family Astroviridae, Mamastravirus genus and have been identified to have five genotypes (PAstV1–5). These viruses are highly prevalent in pigs and can cause enteric disease as well as neurological or respiratory symptoms depending on their genotypes. At present, the epidemiological impacts of some PAstV genotypes on pigs are largely unknown and hence continuously monitoring of these PAstVs may be needed. The purpose of this research was to develop an improved and efficient detection tool for PAstVs and to evaluate the developed method using clinical samples. Initially, a set of five chimeric primers (CP), each comprising genotype specific primer pairs with an identical universal adapter at the 5’ end, and a universal primer (UP) that is identical to universal adapter sequence, were designed. With these tools in place, a novel multiplex PCR system with universal primer was established for the simultaneous detection of the five types of PAstV. This method can specifically detect PAstV genotypes, with a limit of detection (LOD) of 5 copies/μL for each genotype irrespective of single or mixed target template. Using this new assay, 273 pig fecal samples were investigated for further assay evaluation. Among all samples, the positive rate was 70.0% with PAstV4 in 56.8% of the samples, PAstV2 in 38.8%, PAstV1 in 16.8%, and PAstV5 in 11.0%. More than one PAstV in a sample were detected in 39.2% of the samples. The consistency rate between the novel multiplex PCR and singleplex PCRs was 96.4–100%. Given its rapidity, specificity and sensitivity, the novel multiplex PCR is a useful approach for demonstrating single or mixed genotype infections of PAstV.

Graphene oxide based ion-imprinted polymers for selective uranium adsorption from seawater

The project for the uranium adsorption from seawater provides a new idea for acquisition of uranium resources. However, the low concentration of uranium in seawater, the presence of multiple metal ions, and the attachment of microorganisms all affect the adsorption of uranium by the material. To solve these problems, graphene oxide (GO)-based uranium imprinted polymers with antibacterial properties and specific selectivity for uranium were prepared. β-1,3 glucan was selected as the functional monomer for grafting GO, followed by the imprinting process with uranyl ions as the target template. After elution of the target ions by hydrochloric acid, the ion-imprinted polymer was prepared (GOG-RE). The results of ion-competition adsorption experiments showed that the imprinted material had a high adsorption selectivity for uranyl ions, the Kd value of 1.28 × 105 mL∙g−1. The calculated maximum adsorption capacity was 806.45 mg·g−1. Antibacterial experiments have proven its significant inhibitory effect on E. coli (Gram-negative), S. Aureus (Gram-positive) and Pseudoalteromonas marina. The above results indicate that GOG-RE has excellent application prospects in the field of uranium adsorption from seawater.

Proactive suppression can be applied to multiple salient distractors in visual search.

There is a growing body of research demonstrating that the capture of attention by a single salient distractor can be prevented via proactive suppression. In real-world contexts, there are often several distracting events that compete for attention, but it is entirely unknown whether multiple objects can be suppressed concurrently. We used behavioral and electrophysiological measures to investigate the existence and time course of multiple-item suppression. We employed search displays that contained either one or two uniquely colored distractors that differed in their salience (S+ and S-), or no such distractors. Search performance improved with the number of salient distractors, indicating that the suppression of multiple items reduced the effective display set size. This was also the case when the target color was no longer fully predictable, ruling out an alternative explanation in terms of attentional guidance by target templates. In an experiment where S+ and S- always appeared together in the same display, the PD component (a marker of proactive suppression) was triggered exclusively by the more salient distractor (S+), indicative of single-item suppression. However, when displays with one or both salient distractors were intermixed, a reliable PD component was also triggered by S-, even when it was accompanied by S+ in the same display. These results show that multiple concurrent salient signals can be proactively inhibited. They demonstrate that signal suppression processes can be adaptively employed to counteract visual distraction at different locations, in order to facilitate the attentional selection of relevant objects in crowded visual environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Efficient and Lightweight Visual Tracking with Differentiable Neural Architecture Search

Over the last decade, Siamese network architectures have emerged as dominating tracking paradigms, which have led to significant progress. These architectures are made up of a backbone network and a head network. The backbone network comprises two identical feature extraction sub-branches, one for the target template and one for the search candidate. The head network takes both the template and candidate features as inputs and produces a local similarity score for the target object in each location of the search candidate. Despite promising results that have been attained in visual tracking, challenges persist in developing efficient and lightweight models due to the inherent complexity of the task. Specifically, manually designed tracking models that rely heavily on the knowledge and experience of relevant experts are lacking. In addition, the existing tracking approaches achieve excellent performance at the cost of large numbers of parameters and vast amounts of computations. A novel Siamese tracking approach called TrackNAS based on neural architecture search is proposed to reduce the complexity of the neural architecture applied in visual tracking. First, according to the principle of the Siamese network, backbone and head network search spaces are constructed, constituting the search space for the network architecture. Next, under the given resource constraints, the network architecture that meets the tracking performance requirements is obtained by optimizing a hybrid search strategy that combines distributed and joint approaches. Then, an evolutionary method is used to lighten the network architecture obtained from the search phase to facilitate deployment to devices with resource constraints (FLOPs). Finally, to verify the performance of TrackNAS, comparison and ablation experiments are conducted using several large-scale visual tracking benchmark datasets, such as OTB100, VOT2018, UAV123, LaSOT, and GOT-10k. The results indicate that the proposed TrackNAS achieves competitive performance in terms of accuracy and robustness, and the number of network parameters and computation volume are far smaller than those of other advanced Siamese trackers, meeting the requirements for lightweight deployment to resource-constrained devices.

A portable system for economical nucleic acid amplification testing.

Introduction: Regular and rapid large-scale screening for pathogens is crucial for controlling pandemics like Coronavirus Disease 2019 (COVID-19). In this study, we present the development of a digital point-of-care testing (POCT) system utilizing microfluidic paper-based analytical devices (μPADs) for the detection of SARS-CoV-2 gene fragments. The system incorporates temperature tuning and fluorescent detection components, along with intelligent and autonomous image acquisition and self-recognition programs. Methods: The developed POCT system is based on the nucleic acid amplification test (NAAT), a well-established molecular biology technique for detecting and amplifying nucleic acids. We successfully detected artificially synthesized SARS-CoV-2 gene fragments, namely ORF1ab gene, N gene, and E gene, with minimal reagent consumption of only 2.2μL per readout, representing a mere 11% of the requirements of conventional in-tube methods. The power dissipation of the system was low, at 6.4W. Results: Our testing results demonstrated that the proposed approach achieved a limit of detection of 1000 copies/mL, which is equivalent to detecting 1 copy or a single RNA template per reaction. By employing standard curve analysis, the quantity of the target templates can be accurately determined. Conclusion: The developed digital POCT system shows great promise for rapid and reliable detection of SARS-CoV-2 gene fragments, offering a cost-effective and efficient solution for controlling pandemics. Its compatibility with other diagnostic techniques and low reagent consumption make it a viable option to enhance healthcare in resource-limited areas.