A hybrid approach for predicting transcription factors.

Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested, and evaluated on a large dataset that contains 19,406 transcription factors and 523,560 non-transcription factor protein sequences. To avoid biases in evaluation, the datasets were divided into training and validation/independent datasets, where 80% of the data was used for training, and the remaining 20% was used for external validation. In the case of alignment-free methods, models were developed using machine learning techniques and the composition-based features of a protein. Our best alignment-free model obtained an AUC of 0.97 on an independent dataset. In the case of the alignment-based method, we used BLAST at different cut-offs to predict the transcription factors. Although the alignment-based method demonstrated excellent performance, it was unable to cover all transcription factors due to instances of no hits. To combine the strengths of both methods, we developed a hybrid method that combines alignment-free and alignment-based methods. In the hybrid method, we added the scores of the alignment-free and alignment-based methods and achieved a maximum AUC of 0.99 on the independent dataset. The method proposed in this study performs better than existing methods. We incorporated the best models in the webserver/Python Package Index/standalone package of "TransFacPred" (https://webs.iiitd.edu.in/raghava/transfacpred).

Uncertainty propagation from probe spacing to Fourier 3-probe straightness measurement.

Reliable and precise straightness profile measurements are crucial for manufacturing ultra-precision components and are capable of further enhancing their accuracy. The Fourier three-probe (F3P) straightness measurement allows for precise assessment of the workpiece profile on the machine by eliminating the harmful influence of the error motion of the sliding table. However, the probe spacing uncertainty deteriorates the measurement accuracy remarkably; and, the affecting mechanism behind this phenomenon has not yet been studied in detail. In this context, this paper thoroughly investigated the propagation of the probe spacing uncertainty in the F3P measurement. First, the influence of the probe spacing deviation is analyzed. Next, by calculating the partial differential of Laplace transform of the workpiece profile, we algebraically deduce the probe spacing uncertainty propagation law, especially in the harmonic domain. Subsequently, Monte Carlo simulations are carried out to confirm the derived propagation law. To reduce uncertainty propagation, a hybrid approach is presented: (I) F3P measurements are carried out under changing probe spacings to produce several sets of Fourier coefficients; (II) optimal harmonic estimates are selected individually according to the harmonic uncertainty. Finally, simulations and experimental measurements are performed for verification.

Hybrid method integrating adsorption and chemical precipitation of heavy metal ions on polymeric fiber surfaces for highly efficient water purification.

A lot of research has been focused on increasing the specific surface area of adsorbents over a long period of time to remove heavy metal ions from wastewater using the adsorbent. However, porous adsorbents with high specific surface area have demonstrated drawbacks in water purification processes, such as high pressure drop and limitations in the adsorption capacity of heavy metal ions. In recent years, a mechanism-based convergence method involving adsorption/chemical precipitation has emerged as a promising strategy to surmount the constraints associated with porous adsorbents. The mechanism involves amine groups on chelating fibers dissociating OH- ions from water molecules, thereby raising the pH near the fibers. This elevated pH promotes the crystallization of heavy metal ions on the fiber surfaces. The removal of heavy metal ions proceeds through a sequence of adsorption and chemical precipitation processes. An adsorbent based on chelating fibers, integrating adsorption technology with chemical precipitation, demonstrates superior performance in removing significant quantities of heavy metal ions (ca. 1000-2000 mg/g for Cd2+, Cu2+ and Pb2+) when compared to developed porous adsorbents (ca. 50-760 mg/g for same ions). This review paper introduces advanced polymer fibers endowed with the capability to integrate hybrid technology, delves into the mechanism of hybrid technology, and examines its application in process technology for the effective removal of heavy metal ions. The versatility of these advanced fibers extends far beyond the removal of heavy metal ions in water treatment, making them poised to garner significant attention from researchers across diverse fields due to their broad range of potential applications. After further processes involving the removal of templates from chelating polymeric fibers used as supports and the reduction of precipitated heavy metal oxide crystals, the resulting heavy metal crystals can exhibit thin walls and well-interconnected porous structures, suitable for catalytic applications.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides.

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Hybrid versus traditional method closure of ventricular septal defects in children.

Objective: The aim of our study was to evaluate the safety and effectiveness of the hybrid method off-pump for closure of isolated ventricular septal defect (VSD) compared with the traditional method of on-pump of children. Methods: This research was a retrospective cohort study. Data were collected from 500 patients with isolated VSD (or residual VSD after a previous repair) who underwent surgery at the National Scientific Medical Center from May 2016 to December 2020. Patients were operated with 1 of 2 methods of surgery: the traditional method of on-pump or the hybrid method of off-pump. This study assessed the safety and efficacy of the hybrid method by comparing it with the traditional method for the treatment of patients with isolated VSD. Results: The procedural success rate reached 93.2% in the hybrid method, with a 6.4% conversion rate to the traditional method and 0.4% hospital mortality. The mean operation time was 84 minutes (31; 160 minutes) in the hybrid group (n = 250) and 168 minutes (70; 300 minutes) in the traditional group (n = 250) (P = .000). Hospital mortality was 0.43% in the first group and 1.5% in the second group (P = .000). Conclusions: The hybrid method of VSD closure is safe and effective in a selected group of patients. The advantages of the hybrid method are improved cosmetics and shorter operation time and overall hospital stay.

A hybrid 2D-FDTD/3D-MoM method used for the analysis of MRI RF coils.

The design of radiofrequency (RF) coils is crucial for ultra-high field (UHF) magnetic resonance imaging (MRI) systems. To analyze RF coils, various numerical methods, such as finite-difference time-domain (FDTD) and method of moments (MoM), are usually adopted. In this paper, we present a novel hybrid approach that combines a two-dimensional (2D) FDTD with a three-dimensional (3D) MoM to analyze MRI RF problems. In our algorithm, the MoM is utilized for calculating the coil current, and FDTD is assigned for solving the electromagnetic (EM) fields in the imaging region. The hybrid method achieves superior efficiency and acceptable accuracy than using either method individually. To validate the hybrid method, we analyze an ellipse coil loaded with a uniform phantom and a realistic human head model, with the objective of tailoring the magnetic field intensity by adding a multilayer dielectric pad (DP). The results show an improvement in the magnetic field after optimizing the DP configuration. These simulation studies indicate the potential of the new numerical method for the design and analysis of RF systems for ultra-high field applications.

A hybrid stochastic interpolation and compression method for kernel matrices.

Kernel functions play an important role in a wide range of scientific computing and machine learning problems. These functions lead to dense kernel matrices that impose great challenges in computational costs at large scale. In this paper, we develop a set of fast kernel matrix compressing algorithms, which can reduce computation cost of matrix operations in the related applications. The foundation of these algorithms is the polyharmonic spline interpolation, which includes a set of radial basis functions that allow flexible choices of interpolating nodes, and a set of polynomial basis functions that guarantee the solvability and convergence of the interpolation. With these properties, original data points in the interacting kernel function can be randomly sampled with great flexibility, so the proposed method is suitable for complicated data structures, such as high-dimensionality, random distribution, or manifold. To further boost the algorithm accuracy and efficiency, this scheme is equipped with a QR sampling strategy, and combined with a recently developed fast stochastic SVD to form a hybrid method. If the overall number of degree of freedom is N, then the compressing algorithm has complexity of O(N) for low-rank matrices, and O(NlogN) for general matrices with a hierarchical structure. Numerical results for data on various domains and different kernel functions validate the accuracy and efficiency of the proposed method.

Optimization of bio-oil extraction from Chlorella biomass via a green approach to obtain algal-based Di-ethyl phthalate.

This study focuses on determining the optimum external operating parameters of algal cell lysis for extraction of bio-oil from Chlorella biomass. Response surface methodology has been applied to a regression analysis model for optimizing solvent ratios, i.e., ethyl acetate to ethanol (E.A.:E) ratio for maximum extraction of bio-oil and aqueous deep eutectic solvent to biomass (aDES:biomass) ratio for algal pretreatment for the enhanced yield of bio-oil. Optimized process conditions were 15 min of homogenization combined with ultrasonication (hybrid method). The aDES:biomass ratio of 8.25 caused the highest cell disruption efficiency to liberate bio-oil from encapsulated cells. The solvent ethyl acetate to ethanol ratio (E.A.:E) was optimum at 0.8 for maximum extraction of bio-oil, and studies indicated a maximum bio-oil yield of 94.0% using this hybrid pretreatment process combined with ultrasonication and homogenization. The GC-MS characterization technique was used to analyze the bio-oil, which showed it consisted of 67.93% Di-ethyl phthalate (DEP) and 32.07% esters compounds (C12-C40 hydrocarbons range). The produced DEP from Chlorella biomass using this sustainable green approach is very promising. The estimated cost was around Rs 49 per gm (equivalent to Rs 664.56 for 13.58 gm), which indicates the potential for a cost-effective method to produce pure DEP from Chlorella biomass.

A new hybrid method combining search and direct based construction ideas to generate all 4 × 4 involutory maximum distance separable (MDS) matrices over binary field extensions.

This article presents a new hybrid method (combining search based methods and direct construction methods) to generate all 4×4 involutory maximum distance separable (MDS) matrices over F2m. The proposed method reduces the search space complexity at the level of n, where n represents the number of all 4×4 invertible matrices over F2m to be searched for. Hence, this enables us to generate all 4×4 involutory MDS matrices over F23 and F24. After applying global optimization technique that supports higher Exclusive-OR (XOR) gates (e.g., XOR3, XOR4) to the generated matrices, to the best of our knowledge, we generate the lightest involutory/non-involutory MDS matrices known over F23, F24 and F28 in terms of XOR count. In this context, we present new 4×4 involutory MDS matrices over F23, F24 and F28, which can be implemented by 13 XOR operations with depth 5, 25 XOR operations with depth 5 and 42 XOR operations with depth 4, respectively. Finally, we denote a new property of Hadamard matrix, i.e., (involutory and MDS) Hadamard matrix form is, in fact, a representative matrix form that can be used to generate a small subset of all 2k×2k involutory MDS matrices, where k > 1. For k = 1, Hadamard matrix form can be used to generate all involutory MDS matrices.

Is the existing methods sustainable? A hybrid approach to flood risk mapping.

The hydraulic and integrated modeling approaches appear to stand out in the sequence of flood risk models that have been presented because of their predictive accuracy. The former has a high probability of under predicting and the latter has a high tendency to over-predict. This study proposed a methodological approach that combines the hydraulic and integrated models using analytical hierarchical raster fusion techniques to strengthen the weaknesses of the individual models. This study seeks to undertake a flood inundation model, a runoff model, and raster fusion models using GIS and HEC-RAS rain-on-grid methods to map flood risk in the Ona river basin of Ibadan city. •A hydraulic model was used to identify flood depth and inundation areas along a major stream channel, which was then extracted, rasterized, resampled, and reclassified to a spatial resolution of 5 m.•Several raster datasets (indicators) were created from land use, elevation, soil, and geological data layers using advanced GIS techniques.•AHP assisted raster data fusion model was used to combine all of the raster indicators into a single consolidated hybrid flood raster layer that revealed flood risk areas by magnitude.

An experimental study of centrifugal microfluidic platforms for magnetic-inertial separation of circulating tumor cells using contraction-expansion and zigzag arrays.

Cancer diagnosis has recently been at the forefront of recent medical research, with ongoing efforts to develop devices and technologies for detecting cancer in patients. One promising approach for cancer diagnosis is the detection of Circulating Tumor Cells (CTCs) in blood samples. Separating these rare cells from the diverse background of blood cells and analyzing them can provide valuable insights into the disease's stage and lethality. Here we present the design and fabrication of a centrifugal microfluidic platform on a polymeric disk that utilizes centrifugal forces for cell isolation. The separation units exploit both active and passive methods. In other words, in addition to introducing novel geometry for channels, an external magnetic field is also employed to separate the target cells from the background cells. In order for the external field to function, the CTCs must first be labeled with antibody-conjugated nanoparticles; the separation process should be then performed. Before the experimental tests, a numerical study was done to determine the optimum parameters; the angular velocity and magnetization investigations showed that 2000 rpm and 868,000 (kA/m) are the optimum conditions for the designed device to reach the efficiency of 100% for both White Blood Cells (WBCs) and CTCs. These results indicate that the passive region of the channels primarily contributes to the focusing of the target cells, and showed that the focusing effect is more pronounced in the expansion-contraction geometry compared to the zigzag geometry. Additionally, the results proved that curved channel geometries performed better than straight ones in terms of separation efficiency. However, if the separation relies solely on channel geometry, the majority of cells would be directed towards the non-target chamber, leading to suboptimal results. This is due to the direction of the forces acting on the cells. However, including an external magnetic field improves the direction of the net force and enhances the separation efficiency. Finally, the numerical and experimental results of the study were compared, and the curved expansion-contraction channel is introduced as the best geometry having 100% and ∼92% CTC separation efficiency, respectively.

Navigating Challenges and Treatment Options in Diaphyseal Forearm Fractures Among Adolescents: Case Series and Narrative Review.

In this paper, we recount the medical trajectories of two male patients, both fourteen years of age, who sustained re-fractures of their radius and ulna six months post their primary diaphyseal fractures. Owing to the limited capacity for growth of the forearm bones between the ages of ten to sixteen years, many queries are engendered concerning apt treatment strategies. The pressing questions are whether these should be conservative or surgical and the precise method to be employed in surgical interventions. This discourse endeavors to demarcate preferred therapeutic options and shed light on a series of standard clinical dilemmas physicians encounter, along with an exhaustive scrutiny of existing literature.

Attenuation Compensation and Estimation.

Estimating the loss of ultrasound signal with propagation depth as a function of frequency is essential for quantifying tissue properties. Specifically, ultrasound attenuation is used to correct for spectral distortion prior to estimating quantitative ultrasound parameters to assess the tissue. Ultrasound attenuation can also be used independently to characterize the tissue. In this chapter, we review the primary algorithms for estimating both the local attenuation within a region of interest as well as the total attenuation between a region of interest and an ultrasound source. The strengths and weaknesses of each algorithm are also discussed.

Hybrid liquid chromatography high resolution and accuracy mass spectrometry approach for quantification of antibody-drug conjugates at the intact protein level in biological samples.

Preclinical in vivo and in vitro characterization of Antibody-Drug Conjugates (ADCs) involves the development of several bioanalytical methods to address many drug exposure questions. The current pharma industry approach requires at least three different assays that must be run, i.e., total antibody (mAb), conjugated payload or conjugated mAb, and free payload assays. Herein we present analytical performances of a quantitative hybrid Ligand Binding/Liquid Chromatography High Resolution and Accuracy Mass Spectrometry (LB/LCHRAM) method that can condense much of the necessary bioanalytical information in one method. The method includes an immuno-capture step, and it detects whole ADC molecules. It was applied to plasma mouse samples and showed reliable bioanalytical performance according to full method validation standards. Quantitation using extracted ion chromatograms and deconvoluted mass peaks was evaluated. The limit of quantitation resulted in 0.5ng of protein on column with a linear dynamic range spanning from 0.5 to 10µg/mL. Moreover, lower drug-to-antibody ratio (DAR) ADC species can be simultaneously detected, also enabling qualitative characterization of in vivo ADC conjugation.

A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling.

The phasmatodea population evolution algorithm (PPE) is a recently proposed meta-heuristic algorithm based on the evolutionary characteristics of the stick insect population. The algorithm simulates the features of convergent evolution, population competition, and population growth in the evolution process of the stick insect population in nature and realizes the above process through the population competition and growth model. Since the algorithm has a slow convergence speed and falls easily into local optimality, in this paper, it is mixed with the equilibrium optimization algorithm to make it easier to avoid the local optimum. Based on the hybrid algorithm, the population is grouped and processed in parallel to accelerate the algorithm's convergence speed and achieve better convergence accuracy. On this basis, we propose the hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE), and this algorithm is compared and tested on the CEC2017, a novel benchmark function suite. The results show that the performance of HP_PPE is better than that of similar algorithms. Finally, this paper applies HP_PPE to solve the AGV workshop material scheduling problem. Experimental results show that HP_PPE can achieve better scheduling results than other algorithms.

Parameter Identification of Model for Piezoelectric Actuators.

Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system's accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm's poor local search capability and the particle swarm optimization algorithm's ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 µm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

TNFepitope: A webserver for the prediction of TNF-α inducing epitopes.

Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts.

ChatGPT, an artificial intelligence chatbot, has rapidly gained prominence in various domains, including medical education and healthcare literature. This hybrid narrative review, conducted collaboratively by human authors and ChatGPT, aims to summarize and synthesize the current knowledge of ChatGPT in the indexed medical literature during its initial four months. A search strategy was employed in PubMed and EuropePMC databases, yielding 65 and 110 papers, respectively. These papers focused on ChatGPT's impact on medical education, scientific research, medical writing, ethical considerations, diagnostic decision-making, automation potential, and criticisms. The findings indicate a growing body of literature on ChatGPT's applications and implications in healthcare, highlighting the need for further research to assess its effectiveness and ethical concerns.

Preparation of wax-based molecularly imprinted monolith for pipette-tip solid-phase extraction: a hybrid method.

The development of molecularly imprinted monolith (MIM) for pipette-tip solid-phase extraction (PT-SPE) for sample pretreatment is challenging . In this work, a wax-based molecularly imprinted monolith (WMIM) was successfully prepared with a hybrid method by integration of the traditional packing SPE column and MIM, including preparation of the salt column inside the pipette, polymerization of wax-based imprinted column (WIC) outside the pipette, and immobilization of WIC inside the pipette tip. To ensure the penetration of samples and solvents during the PT-SPE, micrometer-range interconnected macropores were tailor-made via the salt-template sacrifice method. For the production of high affinity imprinted sites within the WIC, octadecanoic acid was used as functional monomer in the paraffin matrix. In terms of the adsorption property, the synthesized WIC exhibited a specific affinity to cardiovascular drugs, with an imprinting factor (IF) of 4.8 for the target analyte. Moreover, the WMIM-based PT-SPE was coupled with fluorescence spectrophotometry for the target propranolol determination  (the excitation and emission wavelengths were 294 nm and 343 nm, respectively). This analytical method showed high recovery of target detection in different real samples (R > 90%), good sensitivity, and accuracy (R2 = 0.99, LOD = 0.03 ng mL-1). We believe this work could provide a significant contribution  for the fabrication of MIM and promote an emerging trend of developing elution-free materials for sample pretreatment.

A hybrid FDTD/MoM algorithm with a conformal Huygens' equivalent surface for MRI RF coil design and analysis.

Accurate design and analysis of radiofrequency (RF) coils are crucial for ultra-high field (UHF) magnetic resonance imaging (MRI) applications. To improve the numerical accuracy of electromagnetic (EM) simulations, we propose a hybrid finite difference time domain (FDTD)/method of moments (MoM) method. Unlike conventional cuboid-like Huygens' equivalent surfaces (HES), we proposed to use a conformal HES to interface the EM data of the FDTD and MoM zone. The shape and size of the conformal surface can be adjusted to fit different RF coil models, thus broadening the application range of the hybrid FDTD/MoM method. Two numerical models: an 8-channel ellipse array, and an 8-channel bent dipole array, are simulated and compared with the conventional HES counterpart. Numerical results demonstrate the capability of the conformal HES method in the analysis of RF coils.