Mechanistic regulation of planarian shape during growth and degrowth.

Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here, we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth as a result of a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.

Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition.

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

Spin parameter optimization for spin-polarized extended tight-binding methods.

We present an optimization strategy for atom-specific spin-polarization constants within the spin-polarized GFN2-xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. Sensitivity analysis using Sobol indices guides the identification of the most influential parameters for a given reference dataset, allowing for a nuanced understanding of their impact on diverse molecular properties. In the case of the W4-11 dataset, substantial error reduction was achieved, demonstrating the potential of the optimization. Transferability of the optimized spin-polarization constants over different properties, however, is limited, as we demonstrate by applying the optimized parameters on a set of singlet-triplet gaps in carbenes. Further studies on ionization potentials and electron affinities highlight some inherent limitations of current extended tight-binding methods that can not be resolved by simple parameter optimization. We conclude that the significantly improved accuracy strongly encourages the present re-optimization of the spin-polarization constants, whereas the limited transferability motivates a property-specific optimization strategy.

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects.

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations.

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network.

Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the "Symmetric Convolution-based Residual Attention Network (SC-RAN)" is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

Continuous chiral separation process for high-speed countercurrent chromatography established and scaled up: A case of Voriconazole enantioseparation.

An efficient method for the continuous separation of Voriconazole enantiomers was developed using sulfobutyl ether-ß-cyclodextrin (SBE-ß-CD) as a chiral selector in high-speed countercurrent chromatography (HSCCC) with different types. The separation was performed using a two-phase solvent system consisting of n-hexane/ethyl acetate/100 mmol/L phosphate buffer solution (pH = 3.0, containing 50 mmol/L SBE-ß-CD) (1.5:0.5:2, v/v/v). A fast and predictable scale-up process was achieved using an analytical DE HSCCC instrument. The optimized parameters were subsequently applied to a preparative Tauto HSCCC instrument, resulting in consistent separation time and enantiomeric purity, with throughput boosted by a remarkable 11-fold. Preparative HSCCC successfully separated 506 mg of the racemate, delivering enantiomers exceeding 99% purity as confirmed by high-performance liquid chromatography analysis. This investigation presents an effective methodology for forecasting the HSCCC scale-up process and attaining continuous separation of chiral drugs.

Condition optimization for electroporation transfection in horse skeletal muscle satellite cells.

Satellite cells are an important cellular model for studying muscle growth and development and mammalian locomotion-related molecular mechanisms. In this study, we investigated the effects of voltage, pulse duration, and DNA dosage on horse skeletal muscle satellite cells' electroporation transfection efficiency using the eukaryotic expression plasmid Td Tomato-C1 (5.5 kb) encoding the red fluorescent protein gene mainly based on fluorescence-positive cell rate and cell survival rate. By comparison of different voltages, pulse durations, and DNA doses, horse skeletal muscle satellite cells have nearly 80% transfection efficiency under the condition of voltage 120 V, DNA dosage 7 µg/ml, and pulse duration 30 ms. This optimized electroporation condition would facilitate the application of horse skeletal muscle satellite cells in genetic studies of muscle function and related diseases.

Chatter Stability Prediction for Deep-Cavity Turning of a Bent-Blade Cutter.

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending-torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability.

New Solid-State Acoustic Motion Sensors: Sensing Potential Estimation for Different Piezo Plate Materials.

The present paper discusses the scientific and technical problem of optimizing the design and characteristics of a new type of solid-state sensors for motion parameters on bulk acoustic waves in order to increase the signal-to-noise ratio and the detectability of an informative signal against the background of its own noise and interference. Criteria for choosing materials for structural elements, including piezoelectric transducers of the sensitive element, were identified; a corresponding numerical simulation was performed using the developed program; and experimental studies according to the suggested method were carried out to validate the obtained analytical and calculated positions. The experimental results revealed the correctness of the chosen criteria for the optimization of design parameters and characteristics, demonstrated the high correlation between the results of modeling and field studies, and, thus, confirmed the prospects of using this new type of solid-state acoustic sensors of motion parameters in the navigation and control systems of highly dynamic objects.

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed.

The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

Improved Immune Moth-Flame Optimization Based on Gene Correction for Automatic Reverse Parking.

During the process of reverse parking, it is difficult to achieve the ideal reference trajectory while avoiding collision. In this study, with the aim of establishing reference trajectory optimization for automatic reverse parking that smooths and shortens the trajectory length and ensures the berthing inclination angle is small enough, an improved immune moth-flame optimization method based on gene correction is proposed. Specifically, based on the standard automatic parking plane system, a reasonable high-quality reference trajectory optimization model for automatic parking is constructed by combining the cubic spline-fitting method and a boundary-crossing solution based on gene correction integrated into moth-flame optimization. To enhance the model's global optimization performance, nonlinear decline strategies, including crossover and variation probability and weight coefficient, and a high-quality solution-set maintenance mechanism based on fusion distance are also designed. Taking garage No.160 of the Dalian Shell Museum located in Dalian, Xinghai Square, as the experimental site, experiments on automatic parking reference trajectory optimization and tracking control were carried out. The results show that the proposed optimization algorithm provides higher accuracy for reference trajectory optimization and can achieve better tracking control of the reference trajectory.

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction.

BACKGROUND: With the significant reduction in the cost of high-throughput sequencing technology, genomic selection technology has been rapidly developed in the field of plant breeding. Although numerous genomic selection methods have been proposed by researchers, the existing genomic selection methods still face the problem of poor prediction accuracy in practical applications. RESULTS: This paper proposes a genome prediction method MSXFGP based on a multi-strategy improved sparrow search algorithm (SSA) to optimize XGBoost parameters and feature selection. Firstly, logistic chaos mapping, elite learning, adaptive parameter adjustment, Levy flight, and an early stop strategy are incorporated into the SSA. This integration serves to enhance the global and local search capabilities of the algorithm, thereby improving its convergence accuracy and stability. Subsequently, the improved SSA is utilized to concurrently optimize XGBoost parameters and feature selection, leading to the establishment of a new genomic selection method, MSXFGP. Utilizing both the coefficient of determination R2 and the Pearson correlation coefficient as evaluation metrics, MSXFGP was evaluated against six existing genomic selection models across six datasets. The findings reveal that MSXFGP prediction accuracy is comparable or better than existing widely used genomic selection methods, and it exhibits better accuracy when R2 is utilized as an assessment metric. Additionally, this research provides a user-friendly Python utility designed to aid breeders in the effective application of this innovative method. MSXFGP is accessible at https://github.com/DIBreeding/MSXFGP . CONCLUSIONS: The experimental results show that the prediction accuracy of MSXFGP is comparable or better than existing genome selection methods, providing a new approach for plant genome selection.

GalaxyDock2-HEME: Protein-ligand docking for heme proteins.

Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.

A dynamic kinetic model captures cell-free metabolism for improved butanol production.

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors.

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

Boosting the performance of pretrained CNN architecture on dermoscopic pigmented skin lesion classification.

BACKGROUND: Pigmented skin lesions (PSLs) pose medical and esthetic challenges for those affected. PSLs can cause skin cancers, particularly melanoma, which can be life-threatening. Detecting and treating melanoma early can reduce mortality rates. Dermoscopic imaging offers a noninvasive and cost-effective technique for examining PSLs. However, the lack of standardized colors, image capture settings, and artifacts makes accurate analysis challenging. Computer-aided diagnosis (CAD) using deep learning models, such as convolutional neural networks (CNNs), has shown promise by automatically extracting features from medical images. Nevertheless, enhancing the CNN models' performance remains challenging, notably concerning sensitivity. MATERIALS AND METHODS: In this study, we aim to enhance the classification performance of selected pretrained CNNs. We use the 2019 ISIC dataset, which presents eight disease classes. To achieve this goal, two methods are applied: resolution of the dataset imbalance challenge through augmentation and optimization of the training hyperparameters via Bayesian tuning. RESULTS: The performance improvement was observed for all tested pretrained CNNs. The Inception-V3 model achieved the best performance compared to similar results, with an accuracy of 96.40% and an AUC of 0.98. CONCLUSION: According to the study, classification performance was significantly enhanced by augmentation and Bayesian hyperparameter tuning.