Development Of Computational Models Research Articles

Purkinje cell models: past, present and future

The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70′s, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.

РОЗРОБКА ТА ДОСЛІДЖЕННЯ АЛГОРИТМІВ ВИМІРЮВАННЯ ПАРАМЕТРІВ ЕЛЕКТРОЕНЕРГІЇ У РАЗІ ВИКОРИСТАННЯ ФІКСОВАНОЇ ЧАСТОТИ ДИСКРЕТИЗАЦІЇ СИГНАЛІВ

A study of the possibilities of measuring current values, phase shift angles and other parameters of electrical networks using these data, when sampling with a frequency not multiple of the network frequency, was carried out. In the proposed algorithm, an intermediate linear approximation is used to calculate the instantaneous values of interpolated signals at equidistant moments of time during the period of the main frequency of the network. The cases when such sampling is performed on one device with sequential or simultaneous coding of input signals and the case when sampling frequencies do not match in each measurement channel are considered. A study of the use of these methods was conducted using the developed computer model and layout of the distributed measurement system. References 32, figures 19.

Development and Experimental Validation of a Two-stage Thermoelectric Heat Pump Computational Model for Heating Applications

The utilisation of thermoelectric technology as a heat pump in heating applications necessitates comprehensive investigation. The scalable nature of thermoelectric technology enables its operation at elevated temperatures without the requirement of refrigerants. In this work, an accurate computational model that can simulate one- and two-stage thermoelectric heat pumps is developed. This model uses the electric-thermal analogy and the finite difference method, including the thermoelectric effects, temperature dependent properties, thermal contact resistances and all heat exchangers, even the intermediate heat exchanger in the case of a two-stage configuration. Moreover, the model has been experimentally validated by built and tested prototypes, being the first time that a two-stage thermoelectric heat pump model is experimentally validated.The discrepancy between the simulated and experimental results is below the ± 10 % for COP, ± 8 % for generated heat and temperature lift in the airflow, and less than the ± 6 % for consumed power. Additonally, the model simulates real tendencies under different operating conditions, proving the reliability of the developed thermoelectric heat pump model.Finally, the model is used to optimise a thermoelectric system combining one- and two-stage thermoelectric heat pumps, and hybridising them with electric resistances. An airflow of 16.5 m3/h is heated from 25 °C to 160 °C, achieving a maximum COPtot of 1.21. Lastly, the importance of considering the thermal resistances of the heat exchangers is computationally modelled and demonstrated. Not taking them into account would overestimate the performance of the TEHP system by more than the 7 %.

Implementation of a Digital Model of Thermal Characteristics Based on the Temperature Field

Introduction. Computer modeling allows engineers to make valid design decisions by accurately assessing the thermal characteristics of design objects. The implementation of digital twin technology in the process of designing technical facilities is the current direction of scientific research and development. To do this, it is necessary to develop computer models whose accuracy meets the requirements for digital twins. However, the scientific literature does not widely present the results of research aimed at implementing digital twin technology in the design process. The general issues related to the use of digital twins in various industries are mainly considered. Therefore, the objective of this study was the development of a digital model and a comparative analysis of the accuracy of calculations of thermal characteristics of the design object.Materials and Methods. The main tool for conducting the research was the methodology proposed by the authors for developing a computer model of thermal characteristics for the implementation of digital twin technology. The numerical solution was implemented through constructing a thermal model for calculating the temperature field based on the finite element method in the ANSYS engineering analysis system from ANSYS, Inc. (USA). For the analytical solution, a computer model of thermal characteristics developed on the basis of the state-space method, implemented in the ANSYS Twin Builder module, was used. The state-space model was matched to the behavior of the original thermal model through approximating the transfer function to the stepwise response of the thermal load using the time domain vector approximation method. Verification of the constructed analytical model was carried out in the engineering calculation system MATLAB from the MathWorks company (USA). The research was carried out for a 400V machine model manufactured by NPO “Stankostroenie” LLC, Sterlitamak (Russia).Results. The developed digital model makes it possible to calculate the thermal characteristics of the design object with high accuracy. The results of the comparative analysis showed a high degree of correspondence between the values of thermal characteristics obtained using the proposed digital model and the results of numerical simulation. The maximum error in calculating thermal characteristics did not exceed 0.1ºC.Discussion and Conclusion. Computer modeling that combines numerical calculation methods and a scientific approach based on digital twin technology, provides obtaining the result as close as possible to the results of experiments. The digital model proposed in the study is an effective solution, since it provides performing calculations to evaluate thermal characteristics in real time, which is one of the most important requirements for the implementation of digital twin technology.

Multi-Modal in Vitro Experiments Mimicking the Flow Through a Mitral Heart Valve Phantom.

Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with an in vitro phantom setup. An in vitro setup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows were created with a computer-controlled motor-and-pump setup. Catheter pressure measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images. The experimental setup, with 0.5% cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI. A multi-modal set of data for validation of FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.

A quasi-realistic computational model development and flow field study of the human upper and central airways.

The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka's deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka's model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract.

Computer Simulation of a Capillary “Jump” of a Mercury Drop or an Air Bubble Under Conditions of Short-Term Weightlessness

The capillary “jump” of drop of mercury or an air bubble that occurs during the transition of gravity acceleration from earth to microgravity is called a drop of mercury sharp “jump” starting from the bottom of a container with water, where it is located, to its top or a sharp “jump” of the surface of the air shell, which has turned into a spherical air bubble, starting from the top of a container with water down to the bottom of the container. Article presents both a computer model of a capillary “jump” of a mercury drop in a container with water and computer model of a capillary “jump” of an air bubble in a container with water, previously developed by author during the transition of gravitational acceleration from earthly to microgravity. The developed computer models, implemented in the Turbo C and Turbo Basic computer languages, permitted to simulate on the computer display a capillary “jump” of mercury drop and an air bubble in container with water; quantitatively to calculate the energy of motion of a capillary “jump” of a mercury drop and an air bubble in container with water during the transition of the acceleration of gravity from earth to microgravity. The results of calculations of the energy of motion of a capillary “jump” of a mercury drop showed that the surface tension of a mercury drop plays a significant role in the driving of a mercury drop compared to its density. Flight tests have shown that the developed computer models can be successfully used in main systems of spacecraft under conditions of microgravity to predict the occurrence of a capillary “jump” of wide range real mercury drops and air bubbles in these systems.

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs’ powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Computational investigations of Ka-226T helicopter coaxial rotor aerodynamics in the vortex ring state

The vortex ring states are observed when the rotor is flowed at positive angles of attack. For the main rotor, these conditions are realized during a steep descent of the helicopter at low speeds. The rotor vortex ring states are affected by significant phenomena related to the behavior of its aerodynamics, including negative phenomena. The latter is, firstly, referred to decrease in rotor thrust, increase in the required power, pulsations of thrust and torque, unsteady flapping blade motion, etc. In terms of helicopter piloting, it means a sharp loss of altitude, increase in the control force, a high level of vibration, defocusing attenuation of rotor spinning cone, as well as controllability deterioration. All these factors determine the relevance of research on these modes and the importance of solving a problem of defining their boundaries. Recently, due to the rapid development of computer technologies and computational models, it has become practical to perform numerical research of the rotor aerodynamics in vortex ring states. The paper presents the study results of Ka-226T helicopter coaxial rotor aerodynamics in steep descent modes in the field of vortex ring states. The angles of rotor attack αВ = 90...30° and the range of vertical descent velocities Vу = 0...26 m/s are considered. The original nonlinear bladed vortex model of the rotor developed at the Moscow Aviation Institute (MAI) was used. The total and distributed aerodynamic rotor characteristics were calculated. The shapes of the vortex wake and the rotor flow patterns were analyzed. The boundaries of the vortex ring states in velocity coordinates “Vx − Vу” were constructed according to various criteria reflecting the known features of these states. The results obtained significantly complement the existing experience of experimental and numerical research in this field.

Development of test rig for optical diagnostics of cryogenic spray

Spray cooling is the primary method for conducting chilldown and fill of cryogenic propellant tanks. In a typical spray injection process, a liquid sheet/jet that exits the spray nozzle undergoes primary breakup by the development of surface instabilities. Droplets and ligaments generated after the primary breakup undergo secondary breakup to create a dispersion of droplets, which extract heat on impact with the tank walls. In the existing literature, there is limited data on the primary breakup of cryogenic sprays and their detailed visualization. Moreover, an insight into the spray characteristics such as primary breakup length and cone angle is vital to the development of computational models. In this investigation, optical diagnostics of cryogenic spray breakup and measurements of cryogenic spray characteristics have been conducted. Liquid nitrogen is the selected cryogen for the present analysis. To capture the transient nature of spray breakup after the injection, a novel shadowgraph technique is developed to photographically freeze the spray motion. Cryogenic spray characteristics such as cone angle and primary breakup length are obtained from the shadowgraph. In addition, droplet velocity measurement using Particle Image Velocimetry is explored. The comprehensive experimental dataset obtained herein not only provides insights into the mechanism of spray formation for cryogenic fluids but also helps in designing of spray cooling system for tank chilldown.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research

Background Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Regional differences in stomach stretch during organ filling and their implications on the mechanical stress response

As part of the digestive system, the stomach plays a crucial role in the health and well-being of an organism. It produces acids and performs contractions that initiate the digestive process and begin the break-up of ingested food. Therefore, its mechanical properties are of interest. This study includes a detailed investigation of strains in the porcine stomach wall during passive organ filling. In addition, the observed strains were applied to tissue samples subjected to biaxial tensile tests. The results show inhomogeneous strains during filling, which tend to be higher in the circumferential direction (antrum: 13.2%, corpus: 22.0%, fundus: 67.8%), compared to the longitudinal direction (antrum: 4.8%, corpus: 24.7%, fundus: 50.0%) at a maximum filling of 3500 ml. Consequently, the fundus region experienced the greatest strain. In the biaxial tensile experiments, the corpus region appeared to be the stiffest, reaching nominal stress values above 400 kPa in the circumferential direction, whereas the other regions only reached stress levels of below 50 kPa in both directions for the investigated stretch range. Our findings gain new insight into stomach mechanics and provide valuable data for the development and validation of computational stomach models.

An enhanced deep learning approach for vascular wall fracture analysis

AbstractThis work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.

The Virtual Child.

We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity.

Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

Particle separation mechanisms in suspension-feeding fishes: key questions and future directions

Key unresolved questions about particle separation mechanisms in suspension-feeding fishes are identified and discussed, focusing on areas with the potential for substantial future discovery. The published hypotheses that are explored have broad applicability to biological filtration and bioinspired improvements in commercial and industrial crossflow microfiltration processes and microfluidics. As the first synthesis of the primary literature on the particle separation mechanisms of marine, estuarine, and freshwater suspension-feeding fishes, the goals are to enable comparisons with invertebrate suspension-feeding processes, stimulate future theoretical and empirical studies, and further the development of biomimetic physical and computational fluid dynamics models. Of the eight particle separation mechanisms in suspension-feeding fishes, six have been proposed within the past twenty years (inertial lift and shear-induced migration, reduction of effective gap size by vortices, cross-step filtration, vortical flow along outer faces of gill raker plates, ricochet filtration, and lateral displacement). The pace of discovery is anticipated to continue accelerating. Multidisciplinary collaboration and integration among biologists and engineers (including chemical, mechanical, biomedical, and filtration engineering) will result in new perspectives to identify patterns and potential unifying mechanisms across the breadth of suspension-feeding fish taxa, morphology, and function.

What's behind their various activities? Discerning the importance of surface terminations in Ag3PO4 semiconductor from DFT calculations

The surface energy and band gap of inorganic semiconductors are essential properties for a wide variety of applications in materials science, and studying morphologies holds significant importance. However, it is challenging to find a correlation between them at the atomic scale when these materials are multifunctional. This motivates the development of predictive computational models that can optimize these materials based on these quantities. Present findings shed light on the structure and electronic properties of (100), (110), and (111) surfaces of Ag3PO4 by using density functional theory calculations. By introducing two concepts, namely, polyhedron energy and polyhedron band gap, we provide compelling evidence for the effects of surface termination of Ag3PO4 photocatalyst. We compare our predictions to experimental crystal habits, and our results not only allow us to explain their evolution for obtaining a given morphology but also offer a coherent explanation, based on atomic-scale insights, for the discrepancies of morphology-photocatalytic activity relationship of Ag3PO4 in the recent literature. Overall, this work opens a novel avenue for first-principles calculations of morphology and provides a roadmap for the design of more effective inorganic semiconductors with desirable physicochemical properties for potential applications.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.