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The Chinese writing system has several features that make it markedly different from the alphabetic systems that have most often been examined in reading research, including the fact that individual words consist of various uniformly sized, box-shaped characters whose boundaries are not clearly demarcated (e.g., by blank spaces). These features raise the question: How do readers of Chinese "know" where to move their eyes for the purpose of efficiently segmenting and/or identifying words? To answer this question, we used the E-Z Reader model of eye-movement control in reading to run an 'experiment' involving a series of simulations in which two saccade-targeting assumptions (i.e., directing the eyes towards default targets vs. adjusting saccade length as a function of parafoveal processing difficulty) were factorially manipulated with three word-segmentation heuristics (i.e., ideal-observer knowledge of word boundaries vs. probabilistic guessing vs. familiarity-based segmentation) to examine which combination of assumptions provide the best quantitative account of eye-movement control during the reading of Chinese. Based on these simulations, we conclude the best account is one in which readers use relative differences in the familiarity of groups of parafoveal characters to dynamically adjust the lengths of saccades in a manner that affords efficient word identification. We discuss the broader theoretical implications of these conclusions for models of Chinese reading and for models of reading more generally.

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The solubility and thermodynamic properties of the anti-inflammatory drug aceclofenace (ACF) have been assessed in a range of {2-(2-ethoxyethoxy)ethanol (Carbitol) + water} combinations at temperatures ranging from 298.2 K to 318.2 K and atmospheric pressure of 101.1 kPa. The shake flask method was employed to determine the solubility of ACF, and various models including "van't Hoff, Apelblat, Buchowski-Ksiazczak λh, Yalkowsky-Roseman, Jouyban-Acree, and Jouyban-Acree-van't Hoff models" were used to validate the results. The computational models demonstrated a strong correlation with the experimental ACF solubility data, as indicated by the error values of < 3.0%. In the compositions of {Carbitol + water}, the ACF mole fraction solubility was enhanced by temperature and Carbitol mass fraction. The solubility of ACF in mole fraction was found to be lowest in pure water (1.07 × 10- 6 at 298.2 K), and highest in pure Carbitol (1.04 × 10- 1 at 318.2 K). Based on the positive values of the calculated thermodynamic parameters, the dissolution of ACF was determined to be "endothermic and entropy-driven" in all of the {Carbitol + water} solutions that were studied. It was also observed that enthalpy controls the solvation of ACF in solutions containing {Carbitol + water}. ACF-Carbitol had the strongest molecular interactions in contrast to ACF-water. Based on the results of this study, Carbitol holds significant potential for enhancing the solubility of ACF in water.

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CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

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OBJECTIVES: This in vitro study focused on verifying the influence of different ambient light conditions on the accuracy and precision of models obtained from digital scans. METHODOLOGY: To measure the tested illuminances: chair light/reflector; room light, and natural light at the time of scanning, a luxmeter was used. From the STL file, nine experimental groups were formed. RESULTS: Of the nine specific combinations between the three IOS and the three types of lighting, it was verified that for all of them, as well as the ICC, the accuracy was also excellent, in which the measured values were not significantly influenced by the IOS brand (p = 0.994) nor by the type of lighting (p = 0.996). For precision data, GLM indicated a statistically significant interaction between IOS and lighting type. Under LS, accuracy was significantly higher with 3Shape® than with CS 3600 CareStream®, which had significantly higher accuracy than Virtuo Vivo™ Straumann®. CONCLUSIONS: The models obtained with the three IOS evaluated exhibited excellent accuracy under the different illuminance tested and the 3Shape® under the three illuminance conditions was the device that presented the best precision, specifically when using LC and LS.

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Humans are remarkably proficient at the task of distinguishing between symmetric and non-symmetric visual patterns. The neural mechanisms underlying this ability are still unclear. Here we examine symmetry perception along a dimension that can help place some constraints on the nature of these mechanisms. Specifically, we study whether and how human performance on the task of classifying patterns as bilaterally symmetric versus non-symmetric changes as a function of the spatial separation between the flanks. Working with briefly flashed stimuli that embody flank separations of 6 degrees to 54 degrees, we find that classification performance declines significantly with increasing inter-flank distance, but remains well above chance even at the largest separations. Response time registers a progressive increase as the space between the flanks expands. Baseline studies show that these performance changes cannot be attributed solely to reduced acuity in the visual periphery, or increased conduction times for relaying information from those locations. The findings argue for the need to adapt current feedforward models of symmetry perception to be more consistent with the empirical data, and also point to the possible involvement of recurrent processing, as suggested by recent computational results.

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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.

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PURPOSE: Challenges associated with narrative discourse remain consistently observable across the entire spectrum of autism. We analyzed written narratives by autistic and non-autistic adolescents and aimed to investigate narrative writing using quantitative computational methods. METHODS: We employed Natural Language Processing techniques to compare 333 essays from students in the final eighth grade of primary school: 195 written by autistic and 138 by non-autistic participants. RESULTS: Autistic students used words with a positive emotional polarity statistically less frequently (p < .001), and their stories were less abstract (p < .001) than those written by peers from the non-autistic group. However, autistic adolescents wrote more complex stories in terms of readability than participants from the non-autistic group (p < .001). The writing competencies assessed by teachers did not differ significantly between the two groups. CONCLUSION: Findings suggest that written narratives by autistic individuals may exhibit characteristics similar to those detected by computational methods in spoken narratives. Collecting data from national exams and its potential usefulness in distinguishing autistic individuals could pave the way for future large-scale and cost-effective epidemiological studies on autism.

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Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

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Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare.

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Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

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Pore topology and chemistry play crucial roles in the adsorption characteristics of metal-organic frameworks (MOFs). To deepen our understanding of the interactions between MOFs and CO2 during this process, we systematically investigate the adsorption properties of a group of pyrene-based MOFs. These MOFs feature Zn(II) as the metal ion and employ a pyrene-based ligand, specifically 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy). Including different additional ligands leads to frameworks with distinctive structural and chemical features. By comparing these structures, we could isolate the role that pore size, the presence of open-metal sites (OMS), metal-oxygen bridges, and framework charges play in the CO2 adsorption of these MOFs. Frameworks with constricted pore structures display a phenomenon known as the confinement effect, fostering stronger MOF-CO2 interactions and higher uptakes at low pressures. In contrast, entropic effects dominate at elevated pressures, and the MOF's pore volume becomes the driving factor. Through analysis of the CO2 uptakes of the benchmark materials ─some with narrower pores and others with larger pore volumes─it becomes evident that structures with narrower pores and high binding energies excel at low pressures. In contrast, those with larger volumes perform better at elevated pressures. Moreover, this research highlights that open-metal sites and inherent charges within the frameworks of ionic MOFs stand out as CO2-philic characteristics.

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Extracting temporal regularities and relations from experience/observation is critical for organisms' adaptiveness (communication, foraging, predation, prediction) in their ecological niches. Therefore, it is not surprising that the internal clock that enables the perception of seconds-to-minutes-long intervals (interval timing) is evolutionarily well-preserved across many species of animals. This comparative claim is primarily supported by the fact that the timing behavior of many vertebrates exhibits common statistical signatures (e.g., on-average accuracy, scalar variability, positive skew). These ubiquitous statistical features of timing behaviors serve as empirical benchmarks for modelers in their efforts to unravel the processing dynamics of the internal clock (namely answering how internal clock "ticks"). In this chapter, we introduce prominent (neuro)computational approaches to modeling interval timing at a level that can be understood by general audience. These models include Treisman's pacemaker accumulator model, the information processing variant of scalar expectancy theory, the striatal beat frequency model, behavioral expectancy theory, the learning to time model, the time-adaptive opponent Poisson drift-diffusion model, time cell models, and neural trajectory models. Crucially, we discuss these models within an overarching conceptual framework that categorizes different models as threshold vs. clock-adaptive models and as dedicated clock/ramping vs. emergent time/population code models.

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[This corrects the article DOI: 10.3389/fimmu.2024.1368749.].

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Significant progress in the investigation of how prior knowledge influences episodic memory has been made using three sometimes isolated (but not mutually exclusive) approaches: strictly adult behavioral investigations, computational models, and investigations into the development of the system. Here we point out that these approaches are complementary, each approach informs and is informed by the other. Thus, a natural next step for research is to combine all three approaches to further our understanding of the role of prior knowledge in episodic memory. Here we use studies of memory for expectation-congruent and incongruent information from each of these often disparate approaches to illustrate how combining approaches can be used to test and revise theories from the other. This domain is particularly advantageous because it highlights important features of more general memory processes, further differentiates models of memory, and can shed light on developmental change in the memory system. We then present a case study to illustrate the progress that can be made from integrating all three approaches and highlight the need for more endeavors in this vein. As a first step, we also propose a new computational model of memory that takes into account behavioral and developmental factors that can influence prior knowledge and episodic memory interactions. This integrated approach has great potential for offering novel insights into the relationship between prior knowledge and episodic memory, and cognition more broadly.

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Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

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Significance: Near-infrared laser illumination is a non-invasive alternative/complement to classical stimulation methods in neuroscience but the mechanisms underlying its action on neuronal dynamics remain unclear. Most studies deal with high-frequency pulsed protocols and stationary characterizations disregarding the dynamic modulatory effect of sustained and activity-dependent stimulation. The understanding of such modulation and its widespread dissemination can help to develop specific interventions for research applications and treatments for neural disorders. Aim: We quantified the effect of continuous-wave near-infrared (CW-NIR) laser illumination on single neuron dynamics using sustained stimulation and an open-source activity-dependent protocol to identify the biophysical mechanisms underlying this modulation and its time course. Approach: We characterized the effect by simultaneously performing long intracellular recordings of membrane potential while delivering sustained and closed-loop CW-NIR laser stimulation. We used waveform metrics and conductance-based models to assess the role of specific biophysical candidates on the modulation. Results: We show that CW-NIR sustained illumination asymmetrically accelerates action potential dynamics and the spiking rate on single neurons, while closed-loop stimulation unveils its action at different phases of the neuron dynamics. Our model study points out the action of CW-NIR on specific ionic-channels and the key role of temperature on channel properties to explain the modulatory effect. Conclusions: Both sustained and activity-dependent CW-NIR stimulation effectively modulate neuronal dynamics by a combination of biophysical mechanisms. Our open-source protocols can help to disseminate this non-invasive optical stimulation in novel research and clinical applications.

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Humans make use of small differences in the timing of sounds at the two ears-interaural time differences (ITDs)-to locate their sources. Despite extensive investigation, however, the neural representation of ITDs in the human brain is contentious, particularly the range of ITDs explicitly represented by dedicated neural detectors. Here, using magneto- and electro-encephalography (MEG and EEG), we demonstrate evidence of a sparse neural representation of ITDs in the human cortex. The magnitude of cortical activity to sounds presented via insert earphones oscillated as a function of increasing ITD-within and beyond auditory cortical regions-and listeners rated the perceptual quality of these sounds according to the same oscillating pattern. This pattern was accurately described by a population of model neurons with preferred ITDs constrained to the narrow, sound-frequency-dependent range evident in other mammalian species. When scaled for head size, the distribution of ITD detectors in the human cortex is remarkably like that recorded in vivo from the cortex of rhesus monkeys, another large primate that uses ITDs for source localization. The data solve a long-standing issue concerning the neural representation of ITDs in humans and suggest a representation that scales for head size and sound frequency in an optimal manner.

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Striatal dopamine is important in paranoid attributions, although its computational role in social inference remains elusive. We employed a simple game-theoretic paradigm and computational model of intentional attributions to investigate the effects of dopamine D2/D3 antagonism on ongoing mental state inference following social outcomes. Haloperidol, compared with the placebo, enhanced the impact of partner behaviour on beliefs about the harmful intent of partners, and increased learning from recent encounters. These alterations caused substantial changes to model covariation and negative correlations between self-interest and harmful intent attributions. Our findings suggest that haloperidol improves belief flexibility about others and simultaneously reduces the self-relevance of social observations. Our results may reflect the role of D2/D3 dopamine in supporting self-relevant mentalising. Our data and model bridge theory between general and social accounts of value representation. We demonstrate initial evidence for the sensitivity of our model and short social paradigm to drug intervention and clinical dimensions, allowing distinctions between mechanisms that operate across traits and states.

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Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.