Experimental Validation Research Articles

DRAMP 4.0: an open-access data repository dedicated to the clinical translation of antimicrobial peptides

Abstract Antimicrobial peptides (AMPs) are potential candidates for treating multidrug-resistant bacterial infections, yet only a small number of them have progressed into clinical trials. The main challenges include the poor stability and hemolytic/cytotoxic properties of AMPs. Considering this, in the update of the Data Repository of Antimicrobial Peptides (DRAMP), a new annotation on serum and protease stability is added, and special efforts were made to update the hemolytic/cytotoxic information of AMPs. The DRAMP 4.0 currently holds 30 260 entries (8 001 newly added), consisting of 11 612 general entries, 17 886 patent entries, 96 clinical entries, 377 specific entries, 110 entries with stability data, and 179 expanded entries. A total of 2891 entries possess experimentally determined hemolytic activity information, while 2674 entries contain cytotoxicity data by experimental validation. The update also covers new annotations, statistics, categories, functions, and download links. DRAMP is available online at http://dramp.cpu-bioinfor.org/.

Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to identify novel markers for infantile-onset Pompe disease by analyzing key genes and immune cells infiltrating affected skeletal muscles, building on existing research linking its progression to the immune cell infiltration. The datasets GSE38680 and GSE159062 were downloaded and differential expression genes (DEGs) were identified. Potential markers were screened using support vector machine recursive feature elimination (SVM-RFE) analysis and least absolute shrinkage and selection operator (LASSO) regression models. To analyze DEGs and immune processes, 22 immune cell types in affected tissues were examined using CIBERSORT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment in the calcium signaling pathway, phosphatidylinositol signaling system, ubiquitin-mediated proteolysis and JAK-STAT signaling pathway. Machine learning identified GPNMB, CALML6 and TRIM7 as key genes. Immune cell infiltration analysis showed that the expression levels of the above three genes were closely related to immune cells in our target population under study, including T cells regulatory (Tregs), monocytes, macrophages M0, resting and activated dendritic cells, naive and memory B cells. Overall, our study results may provide new clues for exploring markers related to Pompe disease by highlighting key genes and their relationship with immune infiltration, offering insights into the disease’s development. However, previous studies have shown limited evidence of significant immune cell infiltration in muscle biopsies from Pompe patients, and this lack of direct evidence necessitates further investigation and experimental validation in laboratory settings to confirm these associations.

Exploring the antisnake venom Potential of Zingiber officinale and its bioactive compounds against Naja nigricollis venom through computational approaches and experimental validation

Snakebite envenomation remains a significant medical challenge, particularly in tropical and subtropical regions. The present study investigates the inhibitory potential of Zingiber officinale (ginger) and its bioactive compounds against Naja nigricollis venom using in silico approaches and animal models. No protection was observed in the in vivo studies but the extract of the plant was able to prolong the time of death with mean survival time ranging from 2.01 – 2.83 hours. In terms of the in vitro studies, the extract was able to significantly (p<0.05) detoxify the N. nigricollis venom by 80 % at the graded doses; standard antisnake venom (ASV) offered 100 % protection to mice. Molecular docking analysis revealed strong binding affinities between the bioactive compounds and the PLA2 enzyme, indicating potential inhibitory effects. The stability and dynamics of the protein‐ligand complexes were further validated through molecular dynamics (MD) simulations, which confirmed the persistence of these interactions over time. In conclusion, the findings suggest that the bioactive compounds from Z. officinale could serve as promising inhibitors of PLA2, providing a foundation for the development of novel snakebite envenomation therapies

Transient Sand Scour Dynamics Induced by Pulsed Submerged Water Jets: Simulation Analysis

Water jet scouring technology is extensively applied in marine engineering, harbor maintenance, river training, and various other fields, showcasing a broad spectrum of potential applications. However, achieving a comprehensive understanding of the transient sand scouring characteristics of water jets remains challenging due to the inherent complexity of the coupled flow structure involving submerged jets and environmental fluids, along with the intricate dynamics of two-phase flow. This study, rooted in numerical simulation and experimental validation, introduces pulse characteristics into a submerged jet. A thorough investigation is conducted to explore the transient sand scouring characteristics and sand transport laws of the submerged jet under diverse working conditions. The results of this study revealed that the main reason for the asymmetry of the sand pit morphology is not the non-uniform distribution of sand grains, but more likely caused by turbulence effects. Simultaneously, within the initial 0.25 s of the pulse cycle, suspended sediment resulting from the pulsed jet in the preceding cycle gradually transports to the dune and its surrounding areas. Subsequently, from 0.25 s to 0.5 s, sediment on both sides of the pit’s bottom undergoes movement and amalgamation with the sediment that remained unsettled during the previous cycle. The findings reveal that higher jet velocities significantly enhance sediment suspension, migration, and redeposition, leading to deeper erosion and the rapid formation of the sand pit’s outline within 2 s. Additionally, the jet velocity and the impact distance are identified as critical factors influencing erosion depth and sediment dynamics. These insights advance the understanding of erosion mechanisms driven by pulsed jets, highlighting their impact on sediment transport processes. The research findings provide important guidance for dredging and ocean engineering fields and offer a theoretical basis for improving the understanding of submerged jet scouring mechanisms.

Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.

M5C related-regulator-mediated methylation modification patterns and prognostic significance in breast cancer

5-Methylcytosine (m5C) is closely associated with cancer. However, the role of m5C in breast cancer(BC)remains unclear. This study combined single-cell RNA sequencing (scRNA-Seq) and transcriptomics datasets to screen m5C regulators associated with BC progression and analyze their clinical values. Firstly, This study elucidates the mechanisms of the m5C landscape and the specific roles of m5C regulators in BC patients. we found that the dysregulation of m5C regulators with m5Cscore play the essential role of the carcinogenesis and progression in epithelial cells and myeloid cells of BC at single cell level. External validation was conducted using an independent scRNA-Seq datasets. Then, three distinct m5C modification patterns were identified by transcriptomics datasets. Based on the m5C differentially expressed regulators, the m5Cscore was constructed, and used to divide patients with BC into high and low m5Cscore groups. Patients with a high m5Cscore had more abundant immune cell infiltration, stronger antitumor immunity, and better prognoses. Finally, Quantitative real-time (PCR) and immunohistochemistry were used for the in vitro experimental validation, which had extensive prognostic value. In this study, we aimed to assess the expression of m5C regulators involved in BC and investigate their correlation with the tumor microenvironment, clinicopathological characteristics, and prognosis of BC. The m5C regulators could be used to effectively assess the cell specific regulation prognosis of patients with BC and develop more effective immunotherapy strategies.

Peptide‐Guided Self‐Assembly: Fabrication of Tailored Spiral‐Like Nanostructures for Precise Inorganic Templating

AbstractSelf‐assembling peptides (SAPs) are versatile building blocks in the creation of hierarchical nanostructures. Although SAPs promise precise control over assembled morphology and dimensions, experimental validation is still crucial. In this sense, recent efforts have focused on nanospiral structures, which mimic natural architectures and hold potential for biomedical and nanotechnological applications. Here, it is demonstrated that SARS‐CoV‐2 fusion peptides are able to form specific spiral‐like structures using interfacial assembly as a fabrication methodology, along with fine‐tuning of the spiral ensembles through an imposed interfacial flow due to the uniaxial compression in a Langmuir–Blodgett trough. Molecular characterization by atomic force microscopy, neutron reflectometry, interfacial shear rheology, and infrared spectroscopy, highlighted how variations in fusion peptide sequences influence assembly, highlighting the importance of molecular design. These spiral structures are subsequently modified to serve as templates for metallic replicas, expanding the potential of peptide‐guided self‐assembly in fabricating tailored nanostructured surfaces with sub‐10 nm dimensions over cm2 areas.

Kallmann Syndrome: Functional Analysis of a CHD7 Missense Variant Shows Aberrant RNA Splicing

Kallmann syndrome is a rare disorder characterized by hypogonadotropic hypogonadism and an impaired sense of smell (anosmia or hyposmia) caused by congenital defects in the development of the gonadotropin-releasing hormone (GnRH) and olfactory neurons. Mutations in several genes have been associated with Kallmann syndrome. However, genetic testing of this disorder often reveals variants of uncertain significance (VUS) that remain uninterpreted without experimental validation. The aim of this study was to analyze the functional consequences of a heterozygous missense VUS in the CHD7 gene (c.4354G>T, p.Val1452Leu), in a patient with Kallmann syndrome with reversal of hypogonadism. The variant, located in the first nucleotide of exon 19, was analyzed using minigene assays to determine its effect on ribonucleic acid (RNA) splicing. These showed that the variant generates two different transcripts: a full-length transcript with the missense change (p.Val1452Leu), and an abnormally spliced transcript lacking exon 19. The latter results in an in-frame deletion (p.Val1452_Lys1511del) that disrupts the helicase C-terminal domain of the CHD7 protein. The variant was reclassified as likely pathogenic. These findings demonstrate that missense variants can exert more extensive effects beyond simple amino acid substitutions and underscore the critical role of functional analyses in VUS reclassification and genetic diagnosis.

Enhancing Oxygen-Dissolving Capacity of Rotary Drum Food Waste Composting: Tumbling Process Optimization and Experimental Validation with Discrete and Finite Element Methods

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Trajectory Planning and Adaptive Fuzzy PID Control for Precision in Robotic Vertebral Plate Cutting: Addressing Force Dynamics and Deformation Challenges

This study explores trajectory planning and cutting force control for spinal surgical robots, focusing on managing cutting deformation and vibration during vertebral plate cutting—a critical challenge in robotic spinal surgery. Through theoretical analysis, simulation, and experimental validation, the research addresses the complexities of cutting force dynamics and the associated deformations. A dynamic equation for vertebral plate cutting is established, providing a theoretical foundation for trajectory planning. A new trajectory planning method using B-spline curves and an interpolation point constraint formula is introduced, enabling precise robot trajectory control. Additionally, an adaptive fuzzy PID control strategy with force feedback is proposed to dynamically adjust the feed rate, effectively suppressing cutting deformation and vibration in real time. Simulations and experiments validate the effectiveness of these methods in reducing force fluctuations and enhancing control stability. The findings offer valuable guidance for improving the performance and safety of spinal surgery robots, optimizing their trajectories, incorporating dynamic sensing, and enhancing safety controls. Future research should focus on refining control strategies for more complex surgical scenarios and validating their applicability under conditions closer to actual surgical environments. This work provides theoretical and practical insights into advancing robotic spinal surgery, contributing to better surgical outcomes and patient safety.

Numerical Simulations for Calibration Setup for Dynamic Contrast‐Enhanced Ultrasonography Imaging Protocol

ABSTRACTThis study presents an investigation of an innovative microfluidic flow separator using both numerical and experimental approaches to calibrate contrast‐enhanced ultrasound scanners. Numerical simulations were conducted using Lagrangian particles tracking and passive scalar transport methodologies using the OpenFOAM software. The experimental validation confirmed the accuracy of the numerical simulations, particularly at an imposed total pressure of , showing an excellent agreement in particle distributions. The study emphasizes the computational efficiency and modeling of passive scalar transport, providing valuable understanding into the behavior of scalar quantities in microfluidic systems. An optimized diffusion coefficient value of was identified, showing its critical role in achieving accurate simulation results and optimizing the performance of microfluidic flow separators for contrast‐enhanced ultrasound scanner calibration.

Exploring the mechanism of Suxin Hugan Fang in treating ulcerative colitis based on network pharmacology

As a traditional Chinese medicine formula used in clinical practice for an extended period, Suxin-Hugan-Fang (SXHGF) exhibits excellent anti-inflammatory properties. However, the efficacy of SXHGF in treating ulcerative colitis (UC) and its mechanism of action are still unclear. In this study, the therapeutic effects of SXHGF on UC were evaluated using network pharmacology and experimental validation, while also investigating its mechanism of action. By administering DSS to C57BL/6 mice to construct a mouse model of ulcerative colitis, the therapeutic effect of SXHGF on ulcerative colitis was evaluated based on weight loss percentage, disease activity index, colon length changes, and pathological conditions as indicators. The main chemical components of SXHGF were determined by LC-MS-QTOF method. The potential targets and mechanisms of action of SXHGF in the treatment of UC were inferred using bioinformatics methods, and further validated through ELISA, IHC, and Western blotting assays. The experimental results demonstrate that SXHGF can suppress oxidative stress and oxidative damage in the colon tissue of UC mice, and alleviate DSS-induced ulcerative colitis by inhibiting the JAK2/STAT3 and NFκB pathways.

Biospeckle techniques for evaluating the radial growth rate of a fungal colony

Microorganisms play essential roles in the environment, forming symbiotic relationships with plants and animals, though many can be pathogenic and cause significant harm. Investigating microorganism development through monitoring their radial growth offers valuable insights into growth kinetics, biomass, and metabolite synthesis, emphasizing the need for advanced methodologies in radial growth calculation. We utilize laser-based biospeckle techniques to visualize microorganism activity on semi-solid culture media and propose a comprehensive framework to determine the radial growth curve and its rate. This framework efficiently extracts these growth parameters from biospeckle microorganism patterns, as demonstrated through experimental validation. Additionally, we propose a new procedure, to the best of our knowledge, for calculating optimal parameters within the proposed framework, which impacts radial growth calculation. This procedure was tested through a series of experiments, demonstrating its effectiveness in identifying optimal parameters using the biospeckle patterns of microorganism colonies under various conditions. The results highlight the improvement in radial growth calculation when these parameters are applied within the proposed framework.

The Application of PID Control in DC Motor Control Systems

Abstract. The application of Proportional-Integral-Derivative (PID) control in direct current (DC) motor control systems is essential for achieving high levels of precision and reliability in both industrial and robotic applications. As DC motors are widely utilized in environments requiring accurate speed and torque control, this study seeks to address common challenges such as overshoot, instability, and sluggish response times that can compromise performance. The research is centered on the development and implementation of a PID-based control strategy using MATLAB/Simulink as the primary tool for simulation and testing. The study involves detailed simulation models and experimental validation, using data collected from controlled lab environments to evaluate the effectiveness of various tuning methods. These methods include adjusting the proportional, integral, and derivative gains to optimize motor responsiveness and system stability. The results indicate that with appropriate tuning, PID controllers can significantly enhance DC motor performance, leading to reduced overshoot, faster response times, and greater overall system reliability. The research concludes that adaptive PID control strategies, which dynamically adjust parameters in response to changing operating conditions, provide a robust solution for improving DC motor control in complex, dynamic, and nonlinear environments. This study lays the groundwork for future innovations in automation and precision-driven industries, offering insights that could be applied across a wide range of applications.

Monitoring Dairy Cow Rumination Behavior Based on Upper and Lower Jaw Tracking

To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization, establishing it as a detector of the upper and lower jaws of dairy cows. Subsequently, the DeepSort tracking algorithm was utilized to track the upper and lower jaws and plot their movement trajectories. By calculating the difference between the centroid coordinates of the tracking boxes for the upper and lower jaws during rumination, the rumination curve was obtained. Finally, the number of rumination chews and the false detection rate were calculated. The system successfully monitored the frequency of the cows’ chewing actions during rumination. The experimental results indicate that the enhanced network model achieved a mean average precision (mAP@0.5) of 97.5% and 97.9% for the upper and lower jaws, respectively, with precision (P) of 95.4% and 97.4% and recall (R) of 97.6% and 98.4%, respectively. Two methods for determining chewing were proposed, which showed false detection rates of 8.34% and 3.08% after the experimental validation. The research findings validate the feasibility of the jaw movement tracking method, providing a reference for the real-time monitoring of the rumination behavior of dairy cows in group housing environments.

Manipulation of Phototactic Responses by Two-Spotted Spider Mites to Improve Performance of Miticides

Insight into phototactic responses by herbivorous crop pests may be used to manipulate their vertical distribution in crop canopies. Here, we tested the hypothesis that the deployment of specific light sources elicits positive or negative phototactic responses and can be used to enhance performance of miticide applications. We characterized movement responses by two-spotted spider mites (Tetranychus urticae) (spider mites) to seven light sources [ambient (control), UV-C, UV-B, blue, red, white, and near-infrared (NIR)] under experimental conditions. Separate experiments were conducted with/without the presence of a shelter. An analytical approach based on linear regression coefficients (intercept and slope) from observations in ascending order was used. Linear regression coefficients from UV-B indicated significantly negative phototactic bio-response. We examined settlement of spider mites when exposed to 11 light source configurations and with adaxial leaf sides facing either upwards or downwards. This experiment revealed strong positive and negative phototactic bio-responses to blue light and UV-B light, respectively. As a validation experiment, soybean plants were experimentally infested with spider mites and subjected to one of the following three treatments: (1) no treatment (control), (2) miticide [pyrethrins and Beauveria bassiana (BotaniGard Maxx)] only, and (3) a combination of blue and UV-B for 10 min immediately prior to miticide application. Integration of miticide application with prior deployment of blue and UV-B lights significantly increased the performance of miticide application. Results from this study supported the hypothesis. As a pest management approach, the integration of blue light (to elicit positive phototactic response) and UV-B (to elicit negative phototactic response) is believed to be of particular relevance to organic crop producers and/or to producers of crops for which limited numbers of miticides are registered.

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

Numerical modeling and experimental validation of low velocity impact of woven GFRP/CFRP composites

Low-velocity impact of 2D woven glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP) composite laminates was studied experimentally and numerically. Hybrid laminates containing blocked layers of GFRP/CFRP/GFRP with all plies oriented at 0° were investigated. Relatively high impact energies were used to obtain full perforation of the laminate in a low-velocity impact setup. Numerical simulations were carried out using the in-house transient dynamics finite element code, Sierra/SM, developed at Sandia National Laboratories. A three-dimensional continuum damage model was used to describe the response of a woven composite ply. Two methods for handling delamination were considered and compared: (1) cohesive zone modeling and (2) continuum damage mechanics. The reduced model size achieved by omission of the cohesive zone elements produced acceptable results at reduced computational cost. The comparison between different modeling techniques can be used to inform modeling decisions relevant to low velocity impact scenarios. The modeling was validated by comparing with the experimental results and showed good agreement in terms of predicted damage mechanisms and impactor velocity and force histories.

Noise‐robust structural response estimation method using short‐time Fourier transform and long short‐term memory

AbstractStructural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This model enables robust estimations in the presence of noise by positioning an STFT layer before feeding the data into the LSTM layer. The output transformed into the time‐frequency domain by the STFT layer is learned by the LSTM model. The robustness of the proposed model was validated using a numerical model with three degrees of freedom at various signal‐to‐noise ratio levels, and its robustness against impulse and periodic noise was verified. Experimental validation assessed the estimation robustness under impact load and verified the robustness against environmental noise in the acquired acceleration response.

Robust Shared Control With Stable Contact Servoing for Enhanced Object Transportation by Telerobotic Bimanual Mobile Manipulators

ABSTRACTBimanual mobile manipulation significantly enhances the capabilities of field robots that interact with diverse dynamic environments and expands the range of possible manipulation actions. This paper introduces a robust shared control architecture for remote manipulation of the Collaborative Dual‐arm Robot Manipulator (CURI), aimed at stable object transportation. Given that stable contact is pivotal for successful object transportation, our focus is on stabilizing the contact between the robot end‐effectors and the object using a contact servoing strategy. To maintain stable contact, it is essential that the contact wrenches consistently stay within the predefined friction cones throughout the task. We propose a novel shared control strategy to stabilize the contact using the contact parameterization model. This model formulates the contact stability manifold using a set of unconstrained reparameterized variables through a bijective mapping function, ensuring that the controller derived from these unconstrained reparameterized variables consistently maintains contact stability. The efficacy of the proposed approach is convincingly demonstrated through a series of experiments involving representative bimanual transportation tasks within a logistics context. Both theoretical analysis and experimental validations confirm that our proposed control strategy not only ensures the stable transportation of objects by CURI but also substantially enhances the overall robustness of the shared control system.