Initial Template Research Articles

Pep-Whisperer: Inhibitory peptide design.

Designing peptides for protein-protein interaction inhibition is of significant interest in computer-aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi-step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep-Whisperer, which aims to design inhibitory peptides for protein-protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein-protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein-protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand-protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.

Assessment of the ForenSeq mtDNA control region kit and comparison of orthogonal technologies

The ForenSeq® mtDNA Control Region Kit, MiSeq FGx®, and Universal Analysis Software (UAS) were assessed to better define the performance and limitations of the system with forensically relevant samples to provide data for its transition into practice. A total of six MiSeq FGx sequencing runs of ForenSeq mtDNA Control Region kit, three runs of additional orthogonal sequencing chemistries, and Sanger sequencing results for 14 samples were used to test for concordance. Sensitivity, reproducibility, mixture detection studies, as well as studies to measure the performance of amplification and sequencing controls were performed. The use and reliability of the UAS for data analysis was also examined. With a variety of sample types and controls representing many mitochondrial haplotypes, the recently developed mtDNA Control Region Kit, with the MiSeq FGx and UAS, was found to be fit for purpose as reliable, reproducible, and robust. Sensitivity down to 1 pg of input genomic DNA was demonstrated, which allows the system to offer low limits of detection for better interrogation of potential heteroplasmy in samples. Concerns for implementing next generation sequencing (NGS) for mtDNA in laboratories were addressed in this research, including initial template quantification and confirmation of haplotypes generated by UAS software regarding length-based polymorphisms. To improve performance with forensic samples, laboratories could implement mitochondrial-specific qPCR assays for quantification and perform the optional manual normalization protocol. Additional optimization on sample multiplexing can provide methods that either increase sensitivity or cost efficiency of the assay.

Resilient and Sustainable Energy Infrastructure

Background: Critical infrastructure resilience and sustainability are key components of both the 2015 Sendai Framework for Disaster Risk Reduction, as well as the 2015 Sustainable Development Goals (Panda & Ramos, 2020). The recent passing of the 2021 Security Legislation Amendment (Critical Infrastructure) Bill in Australia, coupled with the lack of a governing document at the state level in Tasmania, necessitates a review to uncover the governance settings, which will aide in increasing the resilience and sustainability of energy infrastructures in Tasmania.
 Methods/Design: Following the 2015 PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols), the review will focus on scholarly sources that address the governance of energy infrastructures. An initial deductive data extraction template has been created to help structure data extraction from included studies. In addition to governance settings, secondary evidence will be sought regarding interruptions to energy infrastructures; policy problems and solutions; and resilience and sustainability definitions. Should other themes emerge, the data extraction template will be updated.
 Discussion: Findings from this review will contribute to a more complete understanding of how the resilience and sustainability of energy infrastructures may be increased via deeper knowledge of their governance settings. Tasmanian policy-makers, practitioners, industry specialists, and researchers may use this research to inform and enhance their decision-making on this important topic.

Plasmonic All-Frame-Faceted Octahedral Nanoframes with Eight Engraved Y-Shaped Hot Zones.

We report the synthesis of all-frame-faceted octahedral nanoframes containing eight Y-shaped hot zones in a single entity where electromagnetic near-field focusing can be maximized. To realize such state-of-the-art complex nanoframes, a series of multiple stepwise bottom-up processes were executed by exploiting Au octahedral nanoparticles as the initial template. By rationally controlling the chemical reactivity of different surface facets (i.e., vertexes, edges, and terraces), the Au octahedral nanoparticles went through controlled shape transformations, leading to Au-engraved nanoparticles wherein 24 edges wrap the octahedral Au nanoparticle core. Those edges were then selectively decorated with Pt, leading to the formation of eight Pt tripods in a single entity. After etching the central Au, 3D Pt tripod frame-faceted octahedral nanoframes were achieved with high integrity. By harnessing the obtained Pt nanoframes as a scaffold, AuAg alloy-based plasmonic all-frame-faceted nanoframes were obtained after the co-reduction of Ag and Au, which generated multiple hot zones within multiple surface intra-nanogaps, creating a single-particle, surface-enhanced Raman spectroscopy enhancer platform.

SiamRDT: An Object Tracking Algorithm Based on a Reliable Dynamic Template

Most trackers are only dependent on the first frame as a template to search for and locate the target location in subsequent videos. However, objects may undergo occlusions and deformation over time, and the original snapshot of the object can no longer accurately reflect the current appearance of the object, which greatly limits the performance improvement of the tracker. In this paper, we propose a novel Siamese tracking algorithm with symmetric structure called SiamRDT, which reflects the latest appearance and motion states of objects through additional reliable dynamic templates. The model decides whether to update the dynamic template according to the quality estimation score and employs the attention mechanism to enhance the reliability of the dynamic template, adopting the depth-wise correlation algorithm to integrate the initial template and the dynamic template and the search area. Through reliable dynamic templates and credible initial templates, the model can fuse initial-state information and the latest-state information of objects. We conduct sufficient ablation experiments to illustrate the effectiveness of the proposed key components, and the tracker achieves very competitive results on four large-scale tracking benchmarks, namely OTB100, GOT-10k, LaSOT, and TrackingNet. Our tracker achieves an AO score of 61.3 on GOT-10k, a precision score of 56.5 on LaSOT, a precision score of 69.3 on TrackingNet, and a precision score of 90.5 on OTB100.

Discovery of Potential Neuroprotective Agents against Paclitaxel-Induced Peripheral Neuropathy.

Chemotherapy-induced neurotoxicity is a common adverse effect of cancer treatment. No medication has been shown to be effective in the prevention or treatment of chemotherapy-induced neurotoxicity. Using minoxidil as an initial template for structural modifications in conjunction with an in vitro neurite outgrowth assay, an image-based high-content screening platform, and mouse behavior models, an effective neuroprotective agent CN016 was discovered. Our results showed that CN016 could inhibit paclitaxel-induced inflammatory responses and infiltration of immune cells into sensory neurons significantly. Thus, the suppression of proinflammatory factors elucidates, in part, the mechanism of action of CN016 on alleviating paclitaxel-induced peripheral neuropathy. Based on excellent efficacy in improving behavioral functions, high safety profiles (MTD > 500 mg/kg), and a large therapeutic window (MTD/MED > 50) in mice, CN016 might have great potential to become a peripherally neuroprotective agent to prevent neurotoxicity caused by chemotherapeutics as typified by paclitaxel.

Construction and characterization of two SARS-CoV-2 minigenome replicon systems.

The ongoing COVID‐19 pandemic severely impacts global public health and economies. To facilitate research on severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) virology and antiviral discovery, a noninfectious viral replicon system operating under biosafety level 2 containment is warranted. We report herein the construction and characterization of two SARS‐CoV‐2 minigenome replicon systems. First, we constructed the IVT‐CoV2‐Rep complementary DNA template to generate a replicon messenger RNA (mRNA) with nanoluciferase (NLuc) reporter via in vitro transcription (IVT). The replicon mRNA transfection assay demonstrated a rapid and transient replication of IVT‐CoV2‐Rep in a variety of cell lines, which could be completely abolished by known SARS‐CoV‐2 replication inhibitors. Our data also suggest that the transient phenotype of IVT‐CoV2‐Rep is not due to host innate antiviral responses. In addition, we have developed a DNA‐launched replicon BAC‐CoV2‐Rep, which supports the in‐cell transcription of a replicon mRNA as initial replication template. The BAC‐CoV2‐Rep transient transfection system exhibited a much stronger and longer replicon signal compared to the IVT‐CoV2‐Rep version. We also found that a portion of the NLuc reporter signal was derived from the spliced BAC‐CoV2‐Rep mRNA and was resistant to antiviral treatment, especially during the early phase after transfection. In summary, the established SARS‐CoV‐2 transient replicon systems are suitable for basic and antiviral research, and hold promise for stable replicon cell line development with further optimization.

Tracking by dynamic template: Dual update mechanism

Recently, Siamese trackers have received widespread attention for visual object tracking owing to their good balance between speed and performance. Those Siamese trackers heavily depend on target template while conventional practice fixes the template to initial frame. This strategy makes it unable to cope with variation of target appearance, which often leads to tracking failures and causes the gap in performance from other tracking methods. Despite the performance gain achieved by few template update methods with target templates generated by the tracked results, these tracked templates are easy to accumulate errors and cause tracking drift. In this paper, we propose two template update mechanisms to effectively adapt the target template during the tracking process which is dubbed as DTDU (Dynamic Template with Dual Update). Unlike predecessors that directly use the tracked template, we use initial template to perform similar transformation to the tracked template. Then the similar transformed template and the tracked template are combined linearly to capture the variation of target appearance. These updated templates are stored in a memory bank and retrieved to generate the final target template. In order to enhance quick update of memory bank to accommodate the target appearance, we use the retrieved template to further update the templates in memory bank for subsequent tracking. Extensive experiments on OTB-2015, VOT2016, VOT2018 and GOT-10k datasets have proved the effectiveness of these two update mechanisms and the proposed tracker achieves a real-time speed of 44 fps.

Influence of Gas-Flow Conditions on the Evolution of Thermally Insulating Si3N4 Nano-Felts.

This paper discusses the role of nitrogen (N2) gas flow conditions on the formation of silicon nitride (Si3N4) nano-felts from polysiloxane-impregnated polyurethane (PU) foams. The polymeric foam was converted into an amorphous silicon oxycarbide (SiOC) artefact during pyrolysis, which was then transformed, at a higher temperature, into a Si3N4 felt through a reaction between the decomposition products of SiOC with N2. The study identified that a N2 flux of ~2.60 cm.min−1 at the cross-section of the furnace (controlled to 100 cm3.min−1 at the inlet of the furnace using a flowmeter) substantially favored the transformation of the parent SiOC foam to Si3N4 felts. This process intensification step significantly reduced the wastage and the energy requirement while considering the material production on a bulk scale. The study also inferred that the cell sizes of the initial PU templates influenced the foam to felt transformation.

Learning Temporal-Correlated and Channel- Decorrelated Siamese Networks for Visual Tracking

Recently, Siamese network based trackers have attracted growing popularity in visual tracking, which tackle the tracking by template matching between the initial template and successive search regions. The initial template patch is generally encoded into a convolutional feature for matching. However, the limited representational capability of the template feature limits the tracking accuracy. Besides, this fixed representation also fails to adapt to the target appearance changes. To alleviate these issues, we improve the Siamese trackers by introducing temporal correlation and channel decorrelation mechanisms. On the one hand, we consider the channel-wise correlations between the initial and historical template features to adaptively aggregate informative channel-wise representations for template update. On the other hand, we propose a decorrelation regularization to weaken the channel-wise correlations of individual template features. By end-to-end training, we learn a more complete and adaptive template for accurate object tracking. We demonstrate the generality of our approach by applying it to two prevalent Siamese trackers, i.e., SiamFC and SiamRPN. Extensive experiments on seven benchmark datasets verify the effectiveness of our method.

Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework

Road is one of important traffic lifelines that could be damaged after disaster by landslide rubble, buildings debris, and collapsed branches of trees. Therefore, road damage detection and assessment using post-Disaster High-Resolution Remote Sensing Images is extremely important for finding optimal paths and conducting rescue missions. In an emergency context, the existing methods based on change detection for road damage detection are difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster remote sensing imagery are hard to obtain. In this paper, a novel method based on the Tracking, Learning, and Detector (TLD) framework for detecting the damaged road region from post-disaster high-resolution remote sensing image is presented. First, a spoke wheel operator is employed to define the initial template of road. Then, the TLD framework is used to identify the suspected road damaged areas. Finally, the damaged road areas are extracted by pruning the false damaged roads. The proposed method was evaluated using post-disaster high-resolution remote sensing images collected over Beichuan, China in 2008 and Lushan, China in 2013. The results show that the proposed method is feasible and effective for road damage detection and assessment. Our main conclusion is that such an approach qualifies for practical use.

Photonic density of states and photonic bandgap of deformed titanium dioxide inverse opal structure

Titanium dioxide (TiO2) inverse opal, a well-ordered nanoporous media, has a good potential in light-matter enhancement application. In this work, the fabricated TiO2 inverse opal structures were prepared by well-ordered template from convective deposition. This measured photonic bandgap was shorter in wavelength from the theoretical prediction of the perfect well-ordered pore structure due to structural shrinkage and incomplete matrix fill. Shorter lattice distance from shrinkage and lower refractive index of matrix from incomplete-filled structure resulted in higher eigen energies of photonic crystal. The scanning electron microscope images indicated that the pore size of TiO2 inverse opal was reduced around 39% from the initial template size. Additionally, to explore the detail on photonic bandgap shift of deformed inverse opal, the photonic band-structures and density of states (DOS) spectra under variation of refractive index and fill fraction were evaluated by plane-wave expansion method. It was found that the zero DOS range has a narrow bandwidth at low fill fraction and refractive index of the matrix which agreed with the perturbation theory on the Hermitian Maxwell eigenvalue problem.

Capacity limitations in template-guided multiple color search.

Visual selection of target objects relies on representations of their known features in visual working memory. These representations are referred to as attentional templates. We asked how the capacity of visual working memory relates to the maximal number of attentional templates that can simultaneously guide visual selection. To measure the number of active attentional templates, we used the contingent capture paradigm where cues matching the attentional template have larger effects than cues in a non-matching color. We found larger cueing effects for matching than non-matching cues in one-, two-, and also three-color searches, suggesting that participants can establish up to three attentional templates. However, scrutiny of matching cue trials showed that with three attentional templates, larger cueing effects only occurred when the matching cue had the same color as the actual target. When the matching cue had a possible target color that was different from the actual target color, cueing effects were similar to non-matching cue colors. We assume that processing of a matching cue activates one of the three templates, which inhibits the remaining templates to the level of non-matching colors. With two colors, the inhibition from the activated template is less complete because the initial template activation is higher. Overall, only a maximum of two attentional templates can operate successfully in the contingent capture paradigm. The capacity of template-guided search is therefore far below the capacity of visual working memory.

Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation

This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. After that, through the analysis of the existing methods, compared with some algorithms that only process a single frame in the video, this article analyzes the continuous multiple frames in the video and extracts the continuous multiple frames of the sampling points through the Lucas-Kanade optical flow method. We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. The experiment determines the initial template by comparing the gray-scale variance of the image, uses the characteristic optical flow to estimate the search area of the initial template in the next frame, reduces the matching time, judges the template similarity according to the Hausdorff distance, and uses the adaptive weighted template update method for the templates with large deviations. The simulation results show that the algorithm can achieve long-term stable tracking of moving targets in the mine, and it can also achieve continuous tracking of partially occluded moving targets.

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si3N4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si3N4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.

Dynamic mechanochemical feedback between curved membranes and BAR protein self-organization

In many physiological situations, BAR proteins reshape membranes with pre-existing curvature (templates), contributing to essential cellular processes. However, the mechanism and the biological implications of this reshaping process remain unclear. Here we show, both experimentally and through modelling, that BAR proteins reshape low curvature membrane templates through a mechanochemical phase transition. This phenomenon depends on initial template shape and involves the co-existence and progressive transition between distinct local states in terms of molecular organization (protein arrangement and density) and membrane shape (template size and spherical versus cylindrical curvature). Further, we demonstrate in cells that this phenomenon enables a mechanotransduction mode, in which cellular stretch leads to the mechanical formation of membrane templates, which are then reshaped into tubules by BAR proteins. Our results demonstrate the interplay between membrane mechanics and BAR protein molecular organization, integrating curvature sensing and generation in a comprehensive framework with implications for cell mechanical responses.

SAP-Net: A Simple and Robust 3D Point Cloud Registration Network Based on Local Shape Features.

Point cloud registration is a key step in the reconstruction of 3D data models. The traditional ICP registration algorithm depends on the initial position of the point cloud. Otherwise, it may get trapped into local optima. In addition, the registration method based on the feature learning of PointNet cannot directly or effectively extract local features. To solve these two problems, this paper proposes SAP-Net, inspired by CorsNet and PointNet++, as an optimized CorsNet. To be more specific, SAP-Net firstly uses the set abstraction layer in PointNet++ as the feature extraction layer and then combines the global features with the initial template point cloud. Finally, PointNet is used as the transform prediction layer to obtain the six parameters required for point cloud registration directly, namely the rotation matrix and the translation vector. Experiments on the ModelNet40 dataset and real data show that SAP-Net not only outperforms ICP and CorsNet on both seen and unseen categories of the point cloud but also has stronger robustness.

Understanding Agency in Shared Decision Making: A Qualitative Analysis of Clinical Encounters and Patient-Physician Interviews.

Purpose: Shared decision making is highly encouraged in patient-centered care but can be difficult to implement in practice. Although lack of training has been implicated as an obstacle to successful shared decision making, consensus has not been reached about the most effective educational interventions. 1–3 We propose that the coconstruction of clinical decisions is a complex activity, requiring patient and relational agency, and cultural historical activity theory (CHAT) can be used as a framework to help health professions researchers and educators understand and teach shared decision making. 4 The objective of this qualitative study was to use CHAT to explore, through analysis of clinical encounters and focused interviews, both patient and provider perspectives of the contraceptive decision-making process. A better understanding of shared decision making can inform more effective educational interventions for our learners and potentially improve patient outcomes. Approach/Methods : We recruited female patients between the ages of 17 and 45 who used a contraceptive decision aid mobile application (app) before a visit to a walk-in contraceptive clinic. Physicians providing care to these women in the clinic were also recruited. We audio-recorded the clinical encounter and, following the encounter, we conducted semistructured interviews with each patient and physician separately. We used template analysis to analyze the 63 transcripts (interviews and clinical recordings). For each patient, we considered the collection of their interview, the provider’s interview, and the clinical recording as a single case. Analysis was conducted within and across cases. The initial template was created to guide the identification of elements of CHAT, tensions within and between accounts of the patients, physicians and the clinical encounter, and evidence of patient and relational agency. The template was modified iteratively and new themes were defined and codes added into the template. Results/Outcomes: Twenty-one patients and 8 providers participated, creating 21 cases. Evidence of each CHAT element, including subject, object, tool, community, rules, and division of labor, were identified in at least 1 of the 3 transcripts for each patient–physician dyad. For example, “tools” that were used by patients to achieve the object of obtaining a contraceptive method included their knowledge, previous experience, and the app. We also observed the influence of a patient’s partner or occupation on decision making, which was represented by “community.” By comparing the findings of each element between different cases and by examining occurrences of codes across the transcripts, we identified recurring themes, such as how the app is used as a “tool” in facilitating agency or the impact of both the patient’s and the physician’s “community” on shared decision making. Discussion: By analyzing clinical encounters with CHAT, we identified factors and tensions influencing behavior of individual patients and physicians when engaged in a clinical encounter about contraception. Analysis of interactions of the patient and physician activity systems revealed tensions that were resolved over the course of the encounters and allowed for an exploration of the impact of agency in the process. For example, in several cases, it was the role of “tools” and the physician’s awareness of factors such as their own and the patient’s “communities” that promoted successful shared decision making. Educational interventions that address the complexity of co-constructing decisions may result in more effective shared decision making. Significance: By using CHAT to study shared decision making, we can identify important characteristics of educational interventions for health professionals that may lead to enhanced patient agency and successful shared decision making.

Algorithm for an automatic treatment planning system using a single-arc VMAT for prostate cancer.

Optimization process in treatment planning for intensity‐modulated radiation therapy varies with the treatment planner. Therefore, a large variation in the quality of dose distribution is usually observed. To reduce variation, an automatic optimizing toolkit was developed for the Monaco treatment planning system (Elekta AB, Stockholm, Sweden) for prostate cancer using volumetric‐modulated arc therapy (VMAT). This toolkit was able to create plans automatically. However, most plans needed two arcs per treatment to ensure the dose coverage for targets. For prostate cancer, providing a plan with a single arc was advisable in clinical practice because intrafraction motion management must be considered to irradiate accurately. The purpose of this work was to develop an automatic treatment planning system with a single arc per treatment for prostate cancer using VMAT. We designed the new algorithm for the automatic treatment planning system to use one arc per treatment for prostate cancer in Monaco. We constructed the system in two main steps: (1) Determine suitable cost function parameters for each case before optimization, and (2) repeat the calculation and optimization until the conditions for dose indices are fulfilled. To evaluate clinical suitability, the plan quality between manual planning and the automatic planning system was compared. Our system created the plans automatically in all patients within a few iterations. Statistical differences between the plans were not observed for the target and organ at risk. It created the plans with no human input other than the initial template setting and system initiation. This system offers improved efficiency in running the treatment planning system and human resources while ensuring high‐quality outputs.

Novel drug design and bioinformatics: an introduction

Abstract In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.