Computational Model Research Articles

A Multilevel, Emergent Journey to Unpack Team Process Dynamics

In this essay, I reflect on my journey as a scientist endeavoring to unpack team processes and effectiveness, offering observations, learning points, and insights I gained along the way. I have organized my journey into three phases; (1) developing multilevel theory as a set of meta-theoretical principles that provide a foundation for theory and research on team processes and effectiveness, (2) substantive theory and research focused on learning, regulation processes, development, adaptation and team leadership, and (3) process theorizing, team dynamics, and computational theorizing and modeling. I close with some recommendations for advancing the science of team process dynamics and effectiveness.

Benchmark experimental study on cavitating flow around Clark-Y 11.7 % hydrofoil at various angles of attack under controlled levels of dissolved air

The present research aims to study cavitating flow around a CLARK-Y 11.7 % hydrofoil with variable angles of attack (α) while maintaining controlled levels of dissolved air in the operating fluid, which is water. A series of experiments were conducted using a water tunnel facility, where the cavitation characteristics were measured and observed using sensors and high-speed imaging techniques. The variables studied in the present work are cavitation number (1 ≤ σ ≤ 2.2) and angle of attack (α = 4°, 6°, 8°, 10°, and 12°), with dissolved air levels (DAL) in the range of 9.3 ppm to 13.1 ppm. The dimensionless cavity length decreases significantly with increasing values of σ/α, indicating an inverse relationship where higher cavitation numbers or lower angles of attack result in shorter cavities. The cavity length follows a power-law scaling relationship, with the empirical equation Lmax/C=4.78×σ/α−0.76.Increasing the angle of attack transitions the cavitation nature from stable (Mode I) to dynamic (Mode II) and highly oscillating (Mode III). Larger cavities result in lower Strouhal numbers, which indicates reduced vortex shedding activity. The relationship between the Strouhal number and the normalized cavitation number σ/α is characterized by the power-law equation St=0.041×σ/α0.3. The pressure coefficient at the leading-edge increases with the angle of attack at low cavitation numbers, while higher cavitation numbers lead to greater pressure coefficient differences between the leading and trailing edges.The present study offers an extensive dataset and empirical correlations that may serve as a benchmark framework, which facilitates the validation of computational and experimental models of cavitating flow under similar conditions.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Learning Internal Representations of 3D Transformations From 2D Projected Inputs.

We describe a computational model for inferring 3D structure from the motion of projected 2D points in an image, with the aim of understanding how biological vision systems learn and internally represent 3D transformations from the statistics of their input. The model uses manifold transport operators to describe the action of 3D points in a scene as they undergo transformation. We show that the model can learn the generator of the Lie group for these transformations from purely 2D input, providing a proof-of-concept demonstration for how biological systems could adapt their internal representations based on sensory input. Focusing on a rotational model, we evaluate the ability of the model to infer depth from moving 2D projected points and to learn rotational transformations from 2D training stimuli. Finally, we compare the model performance to psychophysical performance on structure-from-motion tasks.

Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on somatostatin- and parvalbumin-expressing cortical GABAergic neurons

More than 20 recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Kcnt1Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Kcnt1Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Kcnt1Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.

Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on somatostatin- and parvalbumin-expressing cortical GABAergic neurons.

More than 20 recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Kcnt1Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Kcnt1Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Kcnt1Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.

Nanozyme-enhanced enzyme control: Computational strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Abstract Causality is innate to determination of fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modeling to understand structure-functionality connections is extremely rare. This work proposes a fingerprint based on key structural modes for ABO3-type perovskite oxides and its derivatives, combined with causal models, for predicting Kohn-Sham energies. Our study of causal models capture the inherent coupling between structural modes such as rotation, tilt and antiferroelectric displacements, responsible for phase transition, polarization, magnetization and metal-insulator transition, exhibited by these materials. Although developed for modeling specific functionality, this method is universally applicable to derive other functionalities and even different material classes while tracking hidden causal mechanisms via structural distortions.

Gaze behaviors during free viewing revealed differences in visual salience processing across four major psychiatric disorders: a mega-analysis study of 1012 individuals.

Aberrant salience processing has been proposed as a pathophysiological mechanism underlying psychiatric symptoms in patients with schizophrenia. The gaze trajectories of individuals with schizophrenia have been reported to be abnormal when viewing an image, suggesting anomalous visual salience as one possible pathophysiological mechanism associated with psychiatric diseases. This study was designed to determine whether visual salience is affected in individuals with schizophrenia, and whether this abnormality is unique to patients with schizophrenia. We examined the gaze behaviors of 1012 participants recruited from seven institutes (550 healthy individuals and 238, 41, 50 and 133 individuals with schizophrenia, bipolar disorder, major depressive disorder and autism spectrum disorder, respectively) when they looked at stationary images as they liked, i.e., free-viewing condition. We used an established computational model of salience maps derived from low-level visual features to measure the degree to which the gaze trajectories of individuals were guided by visual salience. The analysis revealed that the saliency at the gaze of individuals with schizophrenia were higher than healthy individuals, suggesting that patients' gazes were guided more by low-level image salience. Among the low-level image features, orientation salience was most affected. Furthermore, a general linear model analysis of the data for the four psychiatric disorders revealed a significant effect of disease. This abnormal salience processing depended on the disease and was strongest in patients with schizophrenia, followed by patients with bipolar disorder, major depressive disorder, and autism spectrum disorder, suggesting a link between abnormalities in salience processing and strength/frequency for psychosis of these disorders.

A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time.

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting.

Left atrial appendage closure guided by fusion of 3D computational modelling on real-time fluoroscopy: A multicenter experience

BackgroundPatient-specific 3-dimensional (3D) computational modelling offers a tailored approach with promising results, but experience using digital-twin fusion on real-time fluoroscopy to guide left atrial appendage closure (LAAC) is unreported. ObjectivesTo assess whether LAAC guided by fusion of a 3D computational model on real-time fluoroscopy is safe and effective. MethodsWe included retrospectively through a multicenter registry all consecutive patients with non-valvular atrial fibrillation (AF) who underwent LAAC guided by artificial intelligence (AI)-enabled computer simulations (FEops, Gent, Belgium) fusion with real-time fluoroscopy. Operators selected the appropriate device size and position in relation to the LAA using FEops HEARTguide™, and a digital twin was provided for image fusion. The primary efficacy endpoint was successful LAAC with the use of a single device, without moderate or greater peri-device leak and/or device related thrombus (DRT) on follow-up imaging. The primary safety endpoint was a composite of major procedural complications including tamponade, stroke, systemic embolism, major bleeding, and device embolization. ResultsA total of 106 patients underwent LAAC with an Amulet™ or Watchman FLX™ device using CT-model-fluoroscopy fusion imaging. Device implantation was successful in 100 % of cases. The primary efficacy endpoint was met in 82 patients (89 %). A single-device SINGLE-deployment LAAC procedure was observed in 49 cases (46 %). The primary safety endpoint occurred in 2 patients (1.9 %). After a median follow-up of 405 days, two patients suffered an ischemic stroke and four expired. ConclusionsFusion of a CT-based 3D computational model on real-time fluoroscopy is a safe and effective approach that may optimize transcatheter LAAC outcomes.

Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.

In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI). A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population. By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98). The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.

Rupture Mechanics of Blood Clots: Influence of Fibrin Network Structure on the Rupture Resistance

Embolization is a leading cause of mortality, yet we know little about clot rupture mechanics. Fibrin provides the main structural and mechanical stability to blood clots. Previous studies have shown that altering the concentration of coagulation activators (thrombin or tissue factor (TF)) has a significant impact on fibrin structure and viscoelastic properties, but their effects on rupture properties are mostly unknown. Toughness, which corresponds to the ability to resist rupture, is independent of viscoelastic properties. We used varying TF concentrations to alter the structure and toughness of human plasma clots. We performed single-edge notch rupture tests to examine fibrin toughness under a constant strain rate and we assessed viscoelastic mechanics using rheology. We utilized fluorescent confocal and scanning electron microscopy (SEM) to quantify the fibrin network structure under varying TF concentrations. Our results revealed that increased TF concentration resulted in increased number of fibrin fibers with a reduction in network pore size, thinner and shorter fibrin fibers. Increasing TF concentration yielded a maximum toughness at mid-TF concentration, such that fibrin diameter and number of fibers underlie a complex role in influencing the rupture resistance of blood clots, resulting in a nonmonotonic relationship between TF and toughness. A simple mechanical model, built on our findings from our Fluctuating Spring (FS) computational model, adopted to estimate the fracture toughness (critical energy release rate) as a function of TF predicts trends that are in good agreement with experiments. The differences in mechanical responses point to the importance of studying the structure-function relationships of fibrin networks, which may be predictive of the tendency for embolization. Statement of significanceFibrin, a naturally occurring biomaterial, is the main mechanical and structural scaffold of blood clots that provides the necessary strength and stability to the clot, ensuring effective stemming of bleeding. The rupture of blood clots can result in the blockage of downstream vessels thereby blocking blood flow and oxygen supply. The fibrin network structure has been shown to influence the viscoelastic mechanical properties of clots, but has not been explored for fracture mechanics. Here, we modulate the fibrin network structure by varying the concentration of Tissue Factor (TF). Interestingly, the association between TF concentration and maximum toughness of the clots is non-monotonic. The variations in mechanical responses highlight the importance of studying the structure-function relationships of fibrin networks, as these may predict the tendency for embolization.

Ring only repair of bileaflet mitral valve prolapse with mitral regurgitation: Insights from computational modeling

This study evaluates the efficacy of annuloplasty repair as a standalone procedure for treating bileaflet mitral valve prolapse with mitral regurgitation (MR). Various flexible ring bands for MR of different severities were compared to assess their biomechanical impact and treatment outcomes. Computational beating heart models, based on the Living Heart Human Model, were utilized to simulate annuloplasty repairs. Repairs using bands of varying lengths were modeled on moderate and severe MR cases, considering bileaflet mitral valve prolapse. Key parameters, including regurgitant orifice area (ROA), prolapse severity, coaptation length, leaflet position, and deformation, were computed to compare conditions before and after implantation. Annuloplasty repairs effectively reduced the ROA in both moderate and severe MR cases, achieving complete sealing in selective instances. Additionally, annuloplasty repair corrected bileaflet prolapse, with prolapse severity decreasing as the annular size increased. Successful coaptation was indicated by the expansion of each leaflet’s contact area distribution and percentage in contact with the opposing leaflet. The risk of systolic anterior motion, that may obstruct the left ventricular outflow tract, was minimized, as the anterior leaflet was directed towards the posterior position. In conclusion, annuloplasty repair alone can effectively treat MR when an appropriate band length is selected. It facilitates a significant reduction in ROA, correction of bileaflet prolapse, and improvement in leaflet coaptation. These findings have important clinical implications, potentially offering a less complex surgical treatment avenue and reducing complications in the management of MR.

Comparison of the Neutralization Power of Sotrovimab Against SARS-CoV-2 Variants: Development of a Rapid Computational Method.

The rapid evolution of SARS-CoV-2 imposed a huge challenge on disease control. Immune evasion caused by genetic variations of the SARS-CoV-2 spike protein's immunogenic epitopes affects the efficiency of monoclonal antibody-based therapy of COVID-19. Therefore, a rapid method is needed to evaluate the efficacy of the available monoclonal antibodies against the new emerging variants or potential novel variants. The aim of this study is to develop a rapid computational method to evaluate the neutralization power of anti-SARS-CoV-2 monoclonal antibodies against new SARS-CoV-2 variants and other potential new mutations. The amino acid sequence of the extracellular domain of the spike proteins of the severe acute respiratory syndrome coronavirus (GenBank accession number YP_009825051.1) and SARS-CoV-2 (GenBank accession number YP_009724390.1) were used to create computational 3D models for the native spike proteins. Specific mutations were introduced to the curated sequence to generate the different variant spike models. The neutralization potential of sotrovimab (S309) against these variants was evaluated based on its molecular interactions and Gibbs free energy in comparison to a reference model after molecular replacement of the reference receptor-binding domain with the variant's receptor-binding domain. Our results show a loss in the binding affinity of the neutralizing antibody S309 with both SARS-CoV and SARS-CoV-2. The binding affinity of S309 was greater to the Alpha, Beta, Gamma, and Kappa variants than to the original Wuhan strain of SARS-CoV-2. However, S309 showed a substantially decreased binding affinity to the Delta and Omicron variants. Based on the mutational profile of Omicron subvariants, our data describe the effect of the G339H and G339D mutations and their role in escaping antibody neutralization, which is in line with published clinical reports. This method is rapid, applicable, and of interest to adapt the use of therapeutic antibodies to the treatment of emerging variants. It could be applied to antibody-based treatment of other viral infections.

Computational brain models map diversity embedded in aging and dementia.

Computational brain models map diversity embedded in aging and dementia.

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33 N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Study on Roadway Layout and Supporting Method of High Intensity Mining Disturbance Bottom Coal Recovery Working Face (A Case Study in Xiagou Mine)

After more than ten years of mining, the subsequent replacement resources of 4 # coal in Xiagou Mine are insufficient. In order to ensure the normal production of the mine, Xiagou Coal Mine needs to recycle the lower layer resources of 4 # coal seam. The design issue of the roadway layout and support mode for the bottom coal recovery left by the strong disturbance in the Xiagou Mine has to be resolved. Through theoretical evaluation and computer simulation, this study investigates the appropriate staggered distance of the roadway layout following upper slicing mining as well as the distributed stress law of the roadway under various staggered distances. Finally, the reasonable external offset is determined. Based on the actual working condition of 404 mining area in Xiagou Mine, the matching roadway support design is put forward. Finally, the roadway layout and support design are validated through the use of computer modeling and field monitoring. The findings demonstrate that when there is a significant disruption in the upper layer, the roadway layout of bottom coal recovery should adopt the external staggered layout and the reasonable external staggered distance is 6 m. At this time, the fluctuation degree of nearby rock stress, the non-uniformity of stress distribution and the uniformity of the roadway's deformation under disturbance meet the production needs. It shows that 6m layout and roadway supporting method are more reasonable, which has reference significance for roadway arrangement and support under similar engineering conditions.

Self-assembly of amphiphilic homopolymers grafted onto spherical nanoparticles: complete embedded minimal surfaces and a machine learning algorithm for their recognition.

By means of computer modelling, the self-assembly of amphiphilic A-graft-B macromolecules, grafted onto a spherical nanoparticle, is studied. In a solvent, that is poor for side pendants, the macromolecules self-assemble into thin membrane-like ABBA bilayers deviated from spherical nanoparticles. The bilayers form morphological structures that depend on the grafting density and macromolecular polymerization degree and can be referred to as the classical family of complete embedded minimal surfaces. The plane disk, catenoid, helicoid, Costa and Enneper surfaces as well as "double" helicoid and "complex minimal surface" were identified, and the fields of their stability were defined. The surfaces can be grouped according to the sequences of conformal transformations that transform them into each other. These surfaces arise in different experiments situationally. Results are summarized in a pie diagram constructed using a machine learning algorithm based on matching grafting points with a specially created planar graphic image.

(Conceptual) explanations in logic

Abstract To explain phenomena in the world is a central human activity and one of the main goals of rational inquiry. There are several types of explanation: one can explain by drawing an analogy, as one can explain by dwelling on the causes (see e.g. see [Woodward (2004, Making Things Happen: A Theory of Causal Explanation. Oxford University Press, Oxford)]. Amongst these different kinds of explanation, in the last decade, philosophers have become receptive to those explanations that explain by providing the reasons (or the grounds) why a statement is true; these explanations are often called conceptual explanations (e.g. see [Betti (2010, Explanation in metaphysics and Bolzano's theory of ground and consequence. Logique et analyse, 211:281316)]). The main aim of the paper is to propose a logical account of conceptual explanations. We will do so by using the resources of proof theory, in particular sequent rules analogous to deep inferences ([e.g. see Brunnler (2004, Deep Inference and Symmetry in Classical Proofs. Logoc Verlag)]). The results we provide not only shed light on conceptual explanations themselves, but also on the role that logic and logical tools might play in the burgeoning field of inquiry concerning explanations. Indeed, we conclude the paper by underlining interesting links between the present research and some other existing works on explanations and logic that have arise in recent years, e.g. see [Arieli et al. (2022, Explainable logic-based argumentation. Computational Models of Argument, 353:3243); Darwiche and Hirth (2023, On the (complete) reasons behind decisions. Journal of Logic Language and Information, 32:6388); Piazza, Pulcini, and Sabatini (2023, Abduction as deductive saturation: a proof-theoretic inquiry. Journal of Philosophical Logic, 52:15751602)]. For here it is for the empirical scientist to know the fact and for the mathematical to know the reason why (our emphasis) [Aristotle (1993, Posterior Analytics. Oxford University Press, Oxford)].