Target Structure Research Articles

Click Chemistry with Cell-Permeable Fluorophores Expands the Choice of Bioorthogonal Markers for Two-Color Live-Cell STED Nanoscopy.

STED nanoscopy allows for the direct observation of dynamic processes in living cells and tissues with diffraction-unlimited resolution. Although fluorescent proteins can be used for STED imaging, these labels are often outperformed in photostability by organic fluorescent dyes. This feature is especially crucial for time-lapse imaging. Unlike fluorescent proteins, organic fluorophores cannot be genetically fused to a target protein but require different labeling strategies. To achieve simultaneous imaging of more than one protein in the interior of the cell with organic fluorophores, bioorthogonal labeling techniques and cell-permeable dyes are needed. In addition, the fluorophores should preferentially emit in the red spectral range to reduce the potential phototoxic effects that can be induced by the STED light, which further restricts the choice of suitable markers. In this work, we selected five different cell-permeable organic dyes that fulfill all of the above requirements and applied them for SPIEDAC click labeling inside living cells. By combining click-chemistry-based protein labeling with other orthogonal and highly specific labeling methods, we demonstrate two-color STED imaging of different target structures in living specimens using different dye pairs. The excellent photostability of the dyes enables STED imaging for up to 60 frames, allowing the observation of dynamic processes in living cells over extended time periods at super-resolution.

A Timoshenko-Ehrenfest beam model for simulating Langevin transducer dynamics

The Langevin transducer is a widely used power ultrasonic device across both medical and industrial applications, from orthopaedic surgery to drilling and welding. It is a sandwich-type device which typically consists of piezoelectric ceramic rings between two metallic end-masses. The transducer is commonly operated at a particular resonant mode to deliver ultrasonic vibrations to a target structure of interest, and is generally modelled using approaches including the transfer matrix method and electromechanical equivalent circuit methods. Here, we propose a variational framework to study the dynamics of the Langevin transducer based on the Timoshenko-Ehrenfest beam theory and Hamilton's principle. The variational equation derived from this model is then discretized by the standard finite element method with spectral elements. To verify the proposed one-dimensional electromechanical model, the computed resonance frequencies, or natural frequencies, of the one-dimensional model are compared to those of a three-dimensional finite element model with respect to varying geometry parameters characterizing the transducer. The results of the reduced one-dimensional and full three-dimensional models are then compared to those measured through an experimental investigation consisting of laser Doppler vibrometry. This is undertaken for the first ten eigenfrequencies, including longitudinal and bending modes, where normalized amplitudes and vibration mode shapes are reported. The close correlation between modelling and experiment demonstrates that the proposed one-dimensional electromechanical model can deliver results consistent with the full three-dimensional model and from experiment, thus verifying a rapid and reliable method for studying the free vibrations and dynamics of the Langevin transducer which accounts for the axial vibrations of the piezoelectric ceramic stack in all cases.

TECHNOLOGY AIDED ACTIVITIES FOR JUNIOR HIGH SCHOOL: ORAL COMMUNICATION SKILLS IN FOCUS

This study aims to explore whether students perceive that the technology aided activities for Junior High School support the development of their oral speaking skills, whether students consider that the out-of-class speaking activities contributed to building their level of confidence with respect to using the target structures and vocabulary in the classroom, and the advantages and/or challenges they perceive in using computer-mediated communicative out-of-class activities to improve their speaking skills during the pandemic. A further aim of this study is to investigate whether the use of technology aided activities out-of-class speaking activities supports the development of students willingness to communicate, measured by using students performance on different online activities. The research was conducted in selected public Junior High School who were chosen among Junior High School students on a voluntary basis. The data for this research were collected via in-depth interview, which was conducted at the middle of the study. Also, the study showed that there was a significant contribution to the students willingness to communicate in the target language as they became more confident as well as more comfortable speaking English. KEY WORDS: Technology, aided, English, activities, junior high school, oral communication, skills, focus

Is Syntactic Priming from Multiple Speakers Stronger?

Syntactic priming in dialogue occurs when exposure to a particular syntactic structure implicitly induces a speaker’s subsequent preference for the same syntactic structures in their own speech. Here, we asked whether this priming effect is boosted when individuals are primed by several different speakers as opposed to one. In an initial baseline session involving a picture description task, we assessed adult participants’ production of double object/DO (vs. prepositional/PO) dative and passive (vs. active) transitive structures. Subsequently, participants played a picture description and verification game, in turns, with six other players (confederates). During verification turns, confederates primed participants by using DO and passive utterances. Crucially, participants were primed either by a single confederate (single-speaker priming condition, SSP) or by five confederates (multi-speaker priming condition, MSP). Across conditions, the same priming stimuli were presented in the same order, leaving speaker source/variation as the only different feature. The degree to which participants were primed for the target structures compared to baseline was measured. Results indicated a robust priming effect in both conditions. Nevertheless, the increase in the target structures’ use did not differ significantly between the SSP and MSP conditions, suggesting that speaker variation did not promote stronger priming.

Neuroprotection of Cholinergic Neurons with a Tau Aggregation Inhibitor and Rivastigmine in an Alzheimer's-like Tauopathy Mouse Model.

Basal forebrain cholinergic dysfunction, most likely linked with tau protein aggregation, is a characteristic feature of Alzheimer's disease (AD). Recent evidence suggests that tau protein is a putative target for the treatment of dementia, and the tau aggregation inhibitor, hydromethylthionine mesylate (HMTM), has emerged as a potential disease-modifying treatment. However, its efficacy was diminished in patients already receiving approved acetylcholinesterase inhibitors. In this study, we ask whether this negative interaction can also be mimicked in experimental tau models of AD and whether the underlying mechanism can be understood. From a previous age profiling study, 6-month-old line 1 (L1) tau transgenic mice were characterized by a severe reduction in several cholinergic markers. We therefore assessed whether long-term pre-exposure with the acetylcholinesterase inhibitor rivastigmine alone and in conjunction with the tau aggregation inhibitor HMTM can reverse cholinergic deficits in L1. Rivastigmine and HMTM, and combinations of the two compounds were administered orally for 11 weeks to both L1 and wild-type mice. The brains were sectioned with a focus on the basal forebrain, motor cortex and hippocampus. Immunohistochemical staining and quantification of choline acetyltransferase (ChAT), tyrosine kinase A (TrkA)-positive neurons and relative optical intensity (ROI) for vesicular acetylcholine transporter (VAChT), and acetylcholinesterase (AChE) reactivity confirmed reversal of the diminished cholinergic phenotype of interneurons (nucleus accumbens, striatum) and projection neurons (medial septum, nucleus basalis magnocellularis) by HMTM, to a greater extent than by rivastigmine alone in L1 mice. Combined administration did not yield additivity but, in most proxies, led to antagonistic effects in which rivastigmine decreased the benefits shown with HMTM alone. Local markers (VAChT and AChE) in target structures of the basal forebrain, motor cortex and hippocampal CA3 seemed to be normalized by HMTM, but not by rivastigmine or the combination of both drugs. HMTM, which was developed as a tau aggregation inhibitor, strongly decreased the tau load in L1 mice, however, not in combination with rivastigmine. Taken together, these data confirm a cholinergic phenotype in L1 tau transgenic mice that resembles the deficits observed in AD patients. This phenotype is reversible by HMTM, but at the same time appears to be subject to a homeostatic regulation induced by chronic pre-treatment with an acetylcholinesterase inhibitor, which interferes with the efficacy of HMTM. The strongest phenotypic reversal coincided with a normalization of the tau load in the cortex and hippocampus of L1, suggesting that tau accumulation underpins the loss of cholinergic markers in the basal forebrain and its projection targets.

Self-replication via tile self-assembly

In this paper we present a model containing modifications to the Signal-passing Tile Assembly Model (STAM), a tile-based self-assembly model whose tiles are capable of activating and deactivating glues based on the binding of other glues. These modifications consist of an extension to 3D, the ability of tiles to form “flexible” bonds that allow bound tiles to rotate relative to each other, and allowing tiles of multiple shapes within the same system. We call this new model the STAM*, and we present a series of constructions within it that are capable of self-replicating behavior. Namely, the input seed assemblies to our STAM* systems can encode either “genomes” specifying the instructions for building a target shape, or can be copies of the target shape with instructions built in. A universal tile set exists for any target shape (at scale factor 2), and from a genome assembly creates infinite copies of the genome as well as the target shape. An input target structure, on the other hand, can be “deconstructed” by the universal tile set to form a genome encoding it, which will then replicate and also initiate the growth of copies of assemblies of the target shape. Since the lengths of the genomes for these constructions are proportional to the number of points in the target shape, we also present a replicator which utilizes hierarchical self-assembly to greatly reduce the size of the genomes required. The main goals of this work are to examine minimal requirements of self-assembling systems capable of self-replicating behavior, with the aim of better understanding self-replication in nature as well as understanding the complexity of mimicking it.

Off-site 3D printed concrete beam design and fabrication

This study identifies the need for a prefabricated reinforced 3D printed concrete (3DPC) beam that can integrate into a building system. A novel 3DPC beam for a specified target structure is designed, fabricated, and experimentally tested. The beam design is experimentally investigated by fabricating the beam with design-determined print parameters and performing a set of two large-scale 4-point bending tests which determine the shear and flexural resistance of the beam. Modified Eurocode design equations successfully predict the flexural resistance within 6%, however the shear design equations overpredict the shear resistance by 51%. The study provides a replicable design and fabrication workflow for 3DPC elements, and a feasible 3DPC beam concept that requires shear strengthening before application in the target frame. 3D concrete printing designers are provided with a 3DPC beam solution with the potential to integrate into a building system and are further equipped to design and fabricate future 3DPC building elements.

Serotonin Receptors in Myocardial Infarction: Friend or Foe?

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide and treatment costs pose a major burden on the global health care system. Despite the variety of treatment options, individual recovery can be still poor and the mortality rate, especially in the first few years after the event, remains high. Therefore, intense research is currently focused on identifying novel target molecules to improve the outcome following AMI. One of the potentially interesting targets is the serotonergic system (5-HT system), not at least because of its connection to mental disorders. It is known that patients suffering from AMI have an increased risk of developing depression and vice versa. This implicates that the 5-HT system can be affected in response to AMI and might thus represent a target structure for patients' treatment. This review aims to highlight the importance of the 5-HT system after AMI by describing the role of individual serotonin receptors (5-HTR) in the regulation of physiological and pathophysiological responses. It particularly focuses on the signaling pathways of the serotonin receptors 1, 2, 4, and 7, which are expressed in the cardiovascular system, during disease onset, and the following remodeling process. This overview also emphasizes the importance of the 5-HT system in AMI etiology and highlights 5-HTRs as potential treatment targets.

The individual variability of the dentato-rubro-thalamic tract in the planning of stereotactic operations in patients with tremor

Background. Stereotactic operations on the ventral‑intermediate nucleus of the thalamus (Vim) and the posterior subthalamic area (PSA) are used for the surgical treatment of tremor. Since these structures are invisible in standard magnetic resonance imaging (MRI) regimes, indirect stereotactic guidance is mainly used during operations. MRI tractography allows taking into account the individual variability of the target structures for tremor, visualizing the target directly, but this technique has not yet entered the routine practice of preparing operations.Aim. The aim of the work is to study the variability of the position of the dentato‑rubro‑thalamic tract (DRT), determined according to MRI tractography data, in relation to the main reference points for indirect stereotactic guidance, as well as to the visible landmarks on MRI in FGATIR mode, to assess the validity of the currently used methods of preparing operations in patients with tremor.Materials and methods. Probabilistic MRI tractography of DRT based on the HARDY protocol was performed in 34 patients. Additionally, 3D T1 tomograms were obtained with axial slices with an isotropic voxel size equal to 1 mm, as well as FLAIR sagittal slices with a thickness of 1.12 and a pitch of 0.56 mm. Eleven patients additionally underwent a series of MRI sections according to the FGATIR program with a thickness of 1 mm, without an intersectional gap.Results. A significant variability of the DRT position has been established both in the coordinate system of the anterior and posterior commissures, and in relation to standard targets for indirect stereotactic guidance. In addition, a visible interhemispheric asymmetry of the position of the tracts was revealed. The smallest degree of deviation from the tract was noted for the trajectories of deep brain stimulation electrodes implanted in the caudal zona incerta (cZI) at the level of the maximum diameter of the red nuclei. A high degree of correspondence between the tract and the target zone of prelemniscal radiations (Raprl) was also established on tomograms in the FGATIR mode.Conclusions. The standard target points for the indirect targeting of Vim targets and the cerebello‑thalamic tract in PSA give a deviation of more than 2 mm from DRT in almost half of patients. During the use of cZI as a standard target for indirect guidance in the treatment of tremor, the DRT is located at the zone of stereotactic impact at the level of the 2nd or 3rd contact of the electrode in 76.5 % of cases. FGATIR mode allows visualizing the structure of Raprl, with stereotactic guidance on which the effect on the DRT can be achieved in 86.4 % of cases.

Identification of Serum Interleukin-22 as Novel Biomarker in Pulmonary Hypertension: A Translational Study.

Growing evidence suggests the crucial involvement of inflammation in the pathogenesis of pulmonary hypertension (PH). The current study analyzed the expression of interleukin (IL)-17a and IL-22 as potential biomarkers for PH in a preclinical rat model of PH as well as the serum levels in a PH patient collective. PH was induced by monocrotalin (60 mg/kg body weight s.c.) in 10 Sprague Dawley rats (PH) and compared to 6 sham-treated controls (CON) as well as 10 monocrotalin-induced, macitentan-treated rats (PH_MAC). Lung and cardiac tissues were subjected to histological and immunohistochemical analysis for the ILs, and their serum levels were quantified using ELISA. Serum IL levels were also measured in a PH patient cohort. IL-22 expression was significantly increased in the lungs of the PH and PH_MAC groups (p = 0.002), whereas increased IL17a expression was demonstrated only in the lungs and RV of the PH (p < 0.05) but not the PH_MAC group (p = n.s.). The PH group showed elevated serum concentrations for IL-22 (p = 0.04) and IL-17a (p = 0.008). Compared to the PH group, the PH_MAC group demonstrated a decrease in IL-22 (p = 0.021) but not IL17a (p = n.s.). In the PH patient collective (n = 92), increased serum levels of IL-22 but not IL-17a could be shown (p < 0.0001). This elevation remained significant across the different etiological groups (p < 0.05). Correlation analysis revealed multiple significant relations between IL-22 and various clinical, laboratory, functional and hemodynamic parameters. IL-22 could serve as a promising inflammatory biomarker of PH with potential value for initial diagnosis, functional classification or even prognosis estimation. Its validation in larger patients' cohorts regarding outcome and survival data, as well as the probability of promising therapeutic target structures, remains the object of further studies.

Design Principles for Fast and Efficient Self-Assembly Processes

Self-assembly is a fundamental concept in biology and of significant interest to nanotechnology. Significant progress has been made in characterizing and controlling the properties of the resulting structures, both experimentally and theoretically. However, much less is known about kinetic constraints and determinants of dynamical properties like time efficiency, although these constraints can become severe limiting factors of self-assembly processes. Here, we investigate how the time efficiency and other dynamical properties of reversible self-assembly depend on the morphology (shape) of the building blocks for systems in which the binding energy between the constituents is large. As paradigmatic examples, we stochastically simulate the self-assembly of constituents with triangular, square, and hexagonal morphology into two-dimensional structures of a specified size. We find that the constituents’ morphology critically determines the assembly time and how it scales with the size of the target structure. Our analysis reveals three key structural parameters defined by the morphology: the nucleation size and attachment order, which describe the effective order of the chemical reactions by which clusters nucleate and grow, respectively, and the growth exponent, which determines how the growth rate of an emerging structure scales with its size. Using this characterization, we formulate an effective theory of the self-assembly kinetics, which we show exhibits an inherent scale invariance. This allows us to identify general scaling laws that describe the minimal assembly time as a function of the size of the target structure. We show how these insights on the kinetics of self-assembly processes can be used to design assembly schemes that could significantly increase the time efficiency and robustness of artificial self-assembly processes. Published by the American Physical Society 2024

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

On-site preparation of sandwich plasmonic coupled SERS tape toward pesticide residue determination on food surface.

Asandwich plasmonic coupled surface enhanced Raman spectroscopy (SERS) tape is proposedprepared by peeling the chemical printed silver nanocorals (AgNCs) from Cu sheet with adhesive tape, which can sample targets from food surface and sandwich them between substrates and Cu sheet for SERS detection. The solid-to-solid transformation method for fabricating SERS tapes can effectively avoid the weakening of tape stickiness during the preparation process. The sandwich plasmonic coupled structure of AgNC substrate, targets, and Cu sheet display excellent SERS activity (EF = 1.62 × 107) for sensitive determination of analytes. In addition, due to the high heat conductivity of Cu sheet, the thermal effect of laser irradiation during SERS detection cannot damage the AgNC tapes, which ensures the reproducibility of subsequent quantification. The sandwich plasmonic coupled SERS tape is demonstrated to quantify malachite green (MG) and methyl parathion (MP) with good linear coefficients (> 0.98) by two typical calibration plots under different concentration ranges. The limit of detection (LOD) of the method is 0.17 ng/cm2 and 0.48 μg/cm2 (S/N = 3) for MG and MP. This method can realize the quantitative determination of MP and MG on the surface of fruits and fish scale with recoveries of 93-113%. The satisfactory detection results demonstrate the proposed sandwich plasmonic coupled AgNC tape can be successfully applied toSERS-based point-of-care testing (POCT) for pesticide residue determination, which will provide a new path for designing and constructing SERS tapes.

Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma

Background and PurposeMagnetic resonance (MR)-only radiotherapy (RT) workflow eliminates uncertainties due to computed tomography (CT)-MR image registration, by using synthetic CT (sCT) images generated from MR. This study describes the clinical implementation process, from retrospective commissioning to prospective validation stage of a commercial artificial intelligence (AI)-based sCT product. Evaluation of the dosimetric performance of the sCT is presented, with emphasis on the impact of voxel size differences between image modalities. Materials and methodssCT performance was assessed in glioblastoma RT planning. Dose differences for 30 patients in both commissioning and validation cohorts were calculated at various dose-volume-histogram (DVH) points for target and organs-at-risk (OAR). A gamma analysis was conducted on regridded image plans. Quality assurance (QA) guidelines were established based on commissioning phase results. ResultsMean dose difference to target structures was found to be within ± 0.7 % regardless of image resolution and cohort. OARs’ mean dose differences were within ± 1.3 % for plans calculated on regridded images for both cohorts, while differences were higher for plans with original voxel size, reaching up to −4.2 % for chiasma D2% in the commissioning cohort. Gamma passing rates for the brain structure using the criteria 1 %/1mm, 2 %/2mm and 3 %/3mm were 93.6 %/99.8 %/100 % and 96.6 %/99.9 %/100 % for commissioning and validation cohorts, respectively. ConclusionsDosimetric outcomes in both commissioning and validation stages confirmed sCT’s equivalence to CT. The large patient cohort in this study aided in establishing a robust QA program for the MR-only workflow, now applied in glioblastoma RT at our center.

Recent Advances in Development of Biosensors for Monitoring of Airborne Microorganisms.

The early detection of infectious microorganisms is crucial for preventing and controlling the transmission of diseases. This article provides a comprehensive review of biosensors based on various diagnostic methods for measuring airborne pathogens. This article aims to explore recent advancements in the field of biosensors tailored for the detection and monitoring of airborne microorganisms, offering insights into emerging technologies and their potential applications in environmental surveillance and public health management. The study summarizes the research conducted on novel methods of detecting airborne microorganisms using different biological sensors, as well as the application of signal amplification technologies such as polymerase chain reaction (PCR), immunoassay reactions, molecular imprinted polymers (MIP) technique, lectin and cascade reactions, and nanomaterials. Antibody and PCR detection methods are effective for specific microbial strains, but they have limitations including limited stability, high cost, and the need for skilled operators with basic knowledge of the target structure. Biosensors based on MIP and lectin offer a low-cost, stable, sensitive, and selective alternative to antibodies and PCR. However, challenges remain, such as the detection of small gas molecules by MIP and the lower sensitivity of lectins compared to antibodies. Additionally, achieving high sensitivity in complex environments poses difficulties for both methods. The development of sensitive, reliable, accessible, portable, and inexpensive biosensors holds great potential for clinical and environmental applications, including disease diagnosis, treatment monitoring, and point-of-care testing, offering a promising future in this field. This review presents an overview of biosensor detection principles, covering component identification, energy conversion principles, and signal amplification. Additionally, it summarizes the research and applications of biosensors in the detection of airborne microorganisms. The latest advancements and future trends in biosensor detection of airborne microorganisms are also analyzed.

A REVIEW ON MOLECULAR DOCKING AND ITS APPLICATION

Molecular Docking is powerful computer strategy, critical in drug development, structural biology, and biomolecular interaction researchproviding a thorough grasp of its importance in modern scientific study. Molecular docking involves forecasting how a small molecule, often a potential medication, will interact with a target biomolecule like DNA or a protein. This process examines the spatial and energetic compatibility of the ligand with the active site of the receptor, assisting in the discovery of new drug candidates, refining existing compounds, and understanding the intricate interactions between drugs and receptors. Molecular Docking a computational tool that evaluates and ranks different ligand-receptor conformations based on their binding energies. An accurate scoring function is imperative for distinguishing high-affinity ligands from low-affinity ones, this makes it possible to identify potential medication candidates for validation in experiments. Molecular docking finds application in various domains of Drug Progress, comprisingstructure-based medication development, virtual screening,lead optimization.When creating a medicine based on structure, the target biomolecules three-dimensional structure is employed to guide the ligand selection that will communicate with the region that is active. Virtual examination accelerates the discovery of potential drug properties by rapidly evaluating large compound libraries. Lead optimization, on the other hand, is facilitated through iterative docking studies aimed at enhancing binding affinity and pharmacological properties. The field of molecular docking has seen a proliferation of diverse techniques designed to address the unique challenges of structure-based drug design and biomolecular interaction analysis. This report provides an in-depth examination of thedistinct methodologies of docking, its principles, applications, and their importance in advancing our understanding of molecular interactions in different contexts.The diversity of docking methods arises from the need to accommodate different types of molecules, target structures, and research objectives.

Real world clinical experience using daily intelligence-assisted online adaptive radiotherapy for head and neck cancer

BackgroundAdaptive radiation therapy (ART) offers a dynamic approach to address structural and spatial changes that occur during radiotherapy (RT) for locally advanced head and neck cancers. The integration of daily ART with Cone-Beam CT (CBCT) imaging presents a solution to enhance the therapeutic ratio by addressing inter-fractional changes.MethodsWe evaluated the initial clinical experience of daily ART for patients with head and neck cancer using an online adaptive platform with intelligence-assisted workflows on daily CBCT. Treatment included auto-contour and structure deformation of Organs at Risk (OARs) and target structures, with adjustments by the treating physician. Two plans were generated: one based on the initial CT simulation with the edited structures (scheduled) and a re-optimized plan (adaptive). Both plans were evaluated and the superior one approved and delivered. Clinical and dosimetric outcomes were reviewed.ResultsTwenty two patients with head and neck cancers (7 Nasopharynx, 6 Oropharynx, 1 oral cavity, 8 larynx) stages I-IVA were treated with daily ART. 770 adaptive and scheduled radiotherapy plans were generated. 703 (91.3%) adaptive plans were chosen. Median time to deliver ART was 20 minutes (range: 18-23). Adaptive compared to scheduled plans demonstrated improved mean V95 values for the PTV70, PTV59.5, and PTV56 by 1.2%, 7.2%, and 6.0% respectively and a mean 1.4% lower maximum dose in PTV70. Fourteen of 17 OARs demonstrated improved dosimetry with adaptation, with select OARs reaching statistical significance. At a median follow up of 14.1 months, local control was 95.5%, two patients developed metastatic disease and four patients died. 9.1% of patients had acute grade 3 dysphagia and 13.6% had grade 2 chronic xerostomia.DiscussionThese findings provide real world evidence of the feasibility and dosimetric benefit of incorporating daily ART on CBCT in the treatment of head and neck cancer. Prospective study is needed to determine if these dosimetric improvements translate into improved outcomes.

Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

The target and mechanism of ellagic acid (EA) against rotavirus (RV) were investigated by network pharmacology, computational biology, and surface plasmon resonance verification. The target of EA was obtained from 11 databases such as HIT and TCMSP, and RV-related targets were obtained from the Gene Cards database. The relevant targets were imported into the Venny platform to draw a Venn diagram, and their intersections were visualized. The protein–protein interaction networks (PPI) were constructed using STRING, DAVID database, and Cytoscape software, and key targets were screened. The target was enriched by Gene Ontology (GO) and KEGG pathway, and the ‘EA anti-RV target-pathway network’ was constructed. Schrodinger Maestro 13.5 software was used for molecular docking to determine the binding free energy and binding mode of ellagic acid and target protein. The Desmond program was used for molecular dynamics simulation. Saturation mutagenesis analysis was performed using Schrodinger's Maestro 13.5 software. Finally, the affinity between ellagic acid and TLR4 protein was investigated by surface plasmon resonance (SPR) experiments. The results of network pharmacological analysis showed that there were 35 intersection proteins, among which Interleukin-1β (IL-1β), Albumin (ALB), Nuclear factor kappa-B1 (NF-κB1), Toll-Like Receptor 4 (TLR4), Tumor necrosis factor alpha (TNF-α), Tumor protein p53 (TP53), Recombinant SMAD family member 3 (SAMD3), Epidermal growth factor (EGF) and Interleukin-4 (IL-4) were potential core targets of EA anti-RV. The GO analysis consists of biological processes (BP), cellular components (CC), and molecular functions (MF). The KEGG pathways with the highest gene count were mainly related to enteritis, cancer, IL-17 signaling pathway, and MAPK signaling pathway. Based on the crystal structure of key targets, the complex structure models of TP53-EA, TLR4-EA, TNF-EA, IL-1β-EA, ALB-EA, NF-κB1-EA, SAMD3-EA, EGF-EA, and IL-4-EA were constructed by molecular docking (XP mode of flexible docking). The MMGBS analysis and molecular dynamics simulation were also studied. The Δaffinity of TP53 was highest in 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The Δaffinity of TLR4 was highest in 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP). The Δaffinity of TNF-α was highest in 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY). SPR results showed that ellagic acid could bind TLR4 protein specifically. TP53, TLR4, and TNF-α are potential targets for EA to exert anti-RV effects, which may ultimately provide theoretical basis and clues for EA to be used as anti-RV drugs by regulating TLR4/NF-κB related pathways.

Adaptive soft threshold transformer for radar high‐resolution range profile target recognition

AbstractRadar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness.

Dynamic estimation method for pulsar periods based on photon energy distribution folding and image template matching

The accuracy of the pulsar period estimation directly affects the restoration effect of the signal profile. A more accurate pulsar profile will help improve the accuracy of pulsar delay estimation and thereby improve the performance of X-ray pulsar navigation. This paper proposes a pulsar period estimation method based on photon energy distribution folding and image template matching (PETM). This method uses the probability distribution information of photon energy for weighted epoch folding. The one-dimensional (1D) profile information was converted into two-dimensional (2D) image information through reverse space-filling curve (SFC) encoding. Then, a feature matching was performed between the target structure and the template structure. At the same time, the criterion of Pearson correlation coefficient (PCC) was used to quantitatively evaluate the matching effect to estimate the optimal period. The simulation results show that the period estimation accuracy of the PETM method is significantly improved, as compared with the traditional \( method. This work also analyzes the folding effect based on the photon energy distribution model and conducts simulation experiments and comparisons on influencing factors, such as noise interference and data quality. At the same time, we also specifically demonstrated the effectiveness of the PETM method for the glitch phenomenon (i.e., a sudden change in period) of pulsar periods. Finally, we also used China's XPNAV-1 satellite to conduct experiments and analysis of the actual observation data of PSR B0531+21 pulsar within a fixed period of time. The results show that the period estimation accuracy of this method is 4.8190$ns$, which is 50.23&lt;!PCT!&gt; higher than the traditional \( method. The method proposed in this article has the advantages of high estimation accuracy, stable estimation performance, strong anti-interference ability, and excellent dynamic period estimation performance. Therefore, it can further improve the navigation performance of X-ray pulsars.