Towards a general modeling framework of resource competition in cognitive development.

The current paper presents an integrated formal model of typical and atypical development based on the mechanisms of mutualism and resource competition. The mutualistic network model is extended with the dynamics of competition for limited resources, such as time and environmental factors. The proposed model generates patterns that resemble established phenomena in cognitive development: the positive manifold, developmental phases, developmental delays and lack of early indicators in atypical development, developmental regression, and "quasi-autism" caused by extreme environmental deprivation. The presented modeling framework fits a general movement towards formal theory construction in psychology. The model is easy to replicate and develop further, and we offer several avenues for future work.

Post-error slowing: Large scale study in an online learning environment for practising mathematics and language.

The ability to monitor and adjust our performance is crucial for adaptive behaviour, a key component of human cognitive control. One widely studied metric of this behaviour is post-error slowing (PES), the finding that humans tend to slow down their performance after making an error. This study is a first attempt at generalizing the effect of PES to an online adaptive learning environment where children practise mathematics and language skills. This population was of particular interest since the major development of error processing occurs during childhood. Eight million response patterns were collected from 150,000 users aged 5 to 13 years old for 6 months, across 23 different learning activities. PES could be observed in most learning activities and greater PES was associated with greater post-error accuracy. PES also varied as a function of several variables. At the task level, PES was greater when there was less time pressure, when errors were slower, and in learning activities focusing on mathematical rather than language skills. At the individual level, students who chose the most difficult level to practise and had higher skill ability also showed greater PES. Finally, non-linear developmental differences in error processing were found, where the PES magnitude increased from 6 to 9-years-old and decreased from 9 to 13. This study shows that PES underlies adaptive behaviour in an educational context for primary school students.

[Value of radiomics model based on dynamic contrast-enhanced magnetic resonance imaging in differentiation fat-poor angiomyolipoma from alpha-fetoprotein-negative hepatocellular carcinoma in the background of non-cirrhotic liver].

Objective: To explore the value of radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiation fat-poor angiomyolipoma (fp-AML) from alpha-fetoprotein-negative hepatocellular carcinoma (n-HCC) in the background of non-cirrhotic liver. Methods: The complete data of 121 patients from Guangdong Provincial People's Hospital, Zhongshan Hospital Affiliated to Fudan University and Sun Yat-sen University Cancer Center with hepatic fp-AML and n-HCC confirmed by pathology from October 2010 to July 2020 were retrospectively analyzed. Among them, 75 were males and 46 were females, aged from 23 to 80 (55±12) years. A total of 93 patients from Zhongshan Hospital Affiliated to Fudan University were divided into the training cohort (n=75) and internal test cohort (n=18) according to entry time, and the patients of other 2 hospitals were divided into external test cohort (n=28). The radiomics features were extracted from the preoperative triple-phase contrast-enhanced images. The feature selection algorithm based on Joint Mutual Information Maximisation (JMIM) was used to extract the optimal feature subset, and support vector machine (SVM) was used to build the radiomics model. The diagnostic performance of radiomics model was evaluated using the receiver operating characteristic (ROC) curve, and was compared with that of two radiologists. Results: In the internal cohort, the area under the curve (AUC) for the differential diagnosis between fp-AML and n-HCC of the radiomics model was 0.819 (with an accuracy of 72.2%), outperforming than radiologist 1 with 10 years of diagnostic experience (AUC=0.542, P=0.029) and radiologist 2 with 2 years of diagnostic experience (AUC=0.375, P=0.004). In the external cohort, the AUC of the radiomics model was 0.772 (with and accuracy of 71.4%), which was comparable to that of radiologist 1 (AUC=0.661, P=0.442) and better than that of radiologist 2 (AUC=0.400, P=0.008). Conclusion: The radiomics model based on dynamic contrast-enhanced MRI is of high accuracy for preoperatively differentiating hepatic fp-AML from n-HCC in the noncirrhotic liver.

Double responding: A new constraint for models of speeded decision making.

Evidence accumulation models (EAMs) have become the dominant models of speeded decision making, which are able to decompose choices and response times into cognitive parameters that drive the decision process. Several models within the EAM framework contain fundamentally different ideas of how the decision making process operates, though previous assessments have found that these models display a high level of mimicry, which has hindered the ability of researchers to contrast these different theoretical viewpoints. Our study introduces a neglected phenomenon that we term "double responding", which can help to further constrain these models. We show that double responding produces several interesting benchmarks, and that the predictions of different EAMs can be distinguished in standard experiment paradigms when they are constrained to account for the choice response time distributions and double responding behaviour in unison. Our findings suggest that lateral inhibition (e.g., the leaky-competing accumulator) provides models with a universal ability to make accurate predictions for these data. Furthermore, only models containing feed-forward inhibition (e.g., the diffusion model) performed poorly under both of our proposed extensions of the standard EAM framework to double responding, suggesting a general inability of feed-forward inhibition to accurately predict these data. We believe that our study provides an important step forward in further constraining models of speeded decision making, though additional research on double responding is required before broad conclusions are made about which models provide the best explanation of the underlying decision-making process.

Should online math learning environments be tailored to individuals' cognitive profiles?

Online learning environments are well-suited for tailoring the learning experience of children individually and on a large scale. An environment such as Math Garden allows children to practice exercises adapted to their specific mathematical ability; this is thought to maximize their mathematical skills. In the current experiment, we investigated whether learning environments should also consider the differential impact of cognitive load on children's math performance depending on their individual verbal working memory (WM) and inhibitory control (IC) capacity. A total of 39 children (8-11 years old) performed a multiple-choice computerized arithmetic game. Participants were randomly assigned to two conditions where the visibility of time pressure, a key feature in most gamified learning environments, was manipulated. Results showed that verbal WM was positively associated with arithmetic performance in general but that higher IC predicted better performance only when the time pressure was not visible. This effect was mostly driven by the younger children. Exploratory analyses of eye-tracking data (N = 36) showed that when time pressure was visible, children attended more often to the question (e.g., 6 × 8). In addition, when time pressure was visible, children with lower IC, in particular younger children, attended more often to answer options representing operant confusion (e.g., 9 × 4 = 13) and visited more answer options before responding. These findings suggest that tailoring the visibility of time pressure, based on a child's individual cognitive profile, could improve arithmetic performance and may in turn improve learning in online learning environments.

An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments.

Electronic learning systems have received increasing attention because they are easily accessible to many students and are capable of personalizing the learning environment in response to students' learning needs. To that end, using fast and flexible algorithms that keep track of the students' ability change in real time is desirable. Recently, the Elo rating system (ERS) has been applied and studied in both research and practical settings (Brinkhuis & Maris, 2009; Klinkenberg, Straatemeier, & van der Maas in Computers & Education, 57, 1813-1824, 2011). However, such adaptive algorithms face the cold-start problem, defined as the problem that the system does not know a new student's ability level at the beginning of the learning stage. The cold-start problem may also occur when a student leaves the e-learning system for a while and returns (i.e., a between-session period). Because external effects could influence the student's ability level during the period, there is again much uncertainty about ability level. To address these practical concerns, in this study we propose alternative approaches to cold-start issues in the context of the e-learning environment. Particularly, we propose making the ERS more efficient by using an explanatory item response theory modeling to estimate students' ability levels on the basis of their background information and past trajectories of learning. A simulation study was conducted under various conditions, and the results showed that the proposed approach substantially reduces ability estimation errors. We illustrate the approach using real data from a popular learning platform.

Proteomics and Its Application in Forensic Pathology.

Proteomics has become one of the hot topics in modern life sciences. Its application prospects have been confirmed in clinical medical research, such as the discovery of new disease biomarkers, identification of disease-related proteins, and development of new drug targets. However, in the field of forensic science, especially in forensic pathology, it is still in the stage of exploration. This paper reviews the research techniques and the use of proteomics in forensic pathology in domestic and foreign scholars, in order to provide new ideas for the research and application of forensic pathology.

[Clinical outcome of allogeneic hematopoietic stem cell transplantation in the treatment of 9 myeloid leukemia patients with granulocytic sarcoma].

To explore the efficacy and influencing factors of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with myeloid leukemia and granulocytic sarcoma (GS). Clinical outcome including hematopoietic reconstitution, transplant-related complications, survival and relapse were collected and retrospectively analyzed in 9 patients with myeloid leukemia and GS after allo-HSCT. Hematopoiesis reconstitution was achieved in all the 9 recipients. Four cases developed acute graft-versus-host disease (GVHD), and 1 with chronic GVHD. The median follow-up time after transplantation was 10(4-81) months. Only 2 cases survived, the other 7 died of relapse. The median time of relapse after transplantation was 5(3-19) months. Allo-HSCT is relatively effective treatment for patients with myeloid leukemia and GS. Relapse after transplantation remains the major factor of mortality.

Critical slowing down as early warning for the onset and termination of depression.

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression.

Researchers' Intuitions About Power in Psychological Research.

Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers' experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies.

[The efficacy of sorafenib to prevent relapse in patients with FLT3-ITD mutation positive acute myeloid leukemia after allogeneic hematopoietic stem cell transplantation].

To study the efficacy of sorafenib to prevent relapse in patients with FLT3-ITD mutation positive acute myeloid leukemia (AML) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). A total of 7 cases with FLT3-ITD positive AML have received allo-HSCT in our department from May 2013 to January 2015. Six cases were administrated with sorafenib after hematopoietic reconstruction. Another patient relapsed on day 192 past allo-HSCT, then she started to use sorafenib after remission of re-induction regimens. Five patients survived. The median progression free survival was 280(126-366)day. This study suggests that sorafenib might prevent relapse past allo-HSCT and improve survival in patients with FLT3-ITD positive AML.

[A comparative study of unrelated donor and matched-sibling donor allogeneic hematopoietic stem cell transplantation in children and adolescents with acquired severe aplastic anemia].

Objective: To evaluate the efficacy of unrelated donor allogeneic hematopoietic stem cell transplantation(URD allo-HSCT) for children and adolescents with severe aplastic anemia (SAA). Methods: Clinical data of 34 SAA children and adolescents undergoing allo-HSCT were retrospectively analyzed from October 2001 to October 2015. According to the source of donor, the patients were divided into matched sibling donor allo-HSCT group (MSD group) and unrelated donor group (URD group). The clinical outcome of SAA children and adolescents receiving URD allo-HSCT was assessed, and patients in MSD allo-HSCT group were enrolled as control at the same period. Results: The rate of hematopoietic reconstitution, the time of neutrophil and platelet engraftment, incidence of chimerism and graft rejection between two groups were not statistically different.The incidence of acute graft-versus-host disease (GVHD) in URD group was significantly higher than that in MSD group [42.9%(6/14) vs 10.5%(2/19), P=0.047]. The incidence of grade Ⅱ-Ⅳ acute GVHD and chronic GVHD in URD were higher than those in MSD group [21.4%(3/14) vs 5.3%(1/19), P=0.288; 35.7%(5/14) vs 5.3%(1/19), P=0.062, respectively], yet without significant difference between two groups. Other transplant-related complications including pulmonary complications, hemorrhagic cystitis, incidence of EBV and CMV reactivation and venous occlusive disease were comparable with two regimens. Estimated 5-years overall survival (OS) rate and disease free survival (DFS) rate were not statistically significant between URD group and MSD group [(84.4±6.6)% vs (89.4±7.1)%, (82.5±5.4)% vs (82.1±4.3)%; P=0.766, P=0.884, respectively]. Conclusions: By multivariate analysis, the outcome of URD allo-HSCT in SAA children and adolescent is similar to MSD allo-HSCT. It could be an alternative option as the first-line treatment for SAA children and adolescents without HLA matched sibling donors.

Major depressive disorder as a nonlinear dynamic system: bimodality in the frequency distribution of depressive symptoms over time.

BACKGROUND: A defining characteristic of Major Depressive Disorder (MDD) is its episodic course, which might indicate that MDD is a nonlinear dynamic phenomenon with two discrete states. We investigated this hypothesis using the symptom time series of individual patients. METHODS: In 178 primary care patients with MDD, the presence of the nine DSM-IV symptoms of depression was recorded weekly for two years. For each patient, the time-series plots as well as the frequency distributions of the symptoms over 104 weeks were inspected. Furthermore, two indicators of bimodality were obtained: the bimodality coefficient (BC) and the fit of a 1- and a 2-state Hidden Markov Model (HMM). RESULTS: In 66% of the sample, high bimodality coefficients (BC>.55) were found. These corresponded to relatively sudden jumps in the symptom curves and to highly skewed or bimodal frequency distributions. The results of the HMM analyses classified 90% of the symptom distributions as bimodal. CONCLUSIONS: A two-state pattern can be used to describe the course of depression symptoms in many patients. The BC seems useful in differentiating between subgroups of MDD patients based on their life course data.

A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times.

A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.

TRACING THE DEVELOPMENT OF TYPEWRITING SKILLS IN AN ADAPTIVE E-LEARNING ENVIRONMENT.

Typewriting studies which compare novice and expert typists have suggested that highly trained typing skills involve cognitive process with an inner and outer loop, which regulate keystrokes and words, respectively. The present study investigates these loops longitudinally, using multi-level modeling of 1,091,707 keystroke latencies from 62 children (M age=12.6 yr.) following an online typing course. Using finger movement repetition as indicator of the inner loop and words typed as indicator of the outer loop, practicing keystroke latencies resulted in different developmental curves for each loop. Moreover, based on plateaus in the developmental curves, the inner loop seemed to require less practice to develop than the outer loop.

Correlation analysis between starch properties and single nucleotide polymorphisms of waxy genes in common rye (Secale cereale L.).

To understand the relationships between single nucleotide polymorphisms (SNPs) in the waxy gene and starch parameters in common rye, we performed sequence characterization, enzyme activity testing, amylopectin/amylose ratio evaluation, starch property testing, and correlation analysis. Specific primers were used to clone waxy from 20 rye cultivars. Sequence analysis showed that waxy was 2852 bp, including 11 exons, and sequence similarity across the 20 cultivars was over 98%. The Waxy protein showed >95% similarity with those from wheat, rice, and barley, the closest genetic relationship being with wheat Wx-A type. Waxy had multiple SNPs, most of which were located in the exons. Amino acid variants were found to be mainly distributed in the catalytic domain in an imbalanced state. Multi-factor correlation analysis revealed significant correlation among starch pasting parameters in rye flour. The Waxy protein activity was significantly negatively correlated with the amylose content and amylopectin/amylose ratio. However, pasting parameters, Waxy enzyme activity, and amylopectin/amylose content ratio were not correlated. The correlation of SNPs, the key catalytic site of Waxy, with starch parameters and enzyme activity suggested that both starch pasting parameters and Waxy protein activity were influenced by No. 260 amino acid (aa). Further, the 141 and 152 aa loci were found in the enzyme-catalyzing domain of Waxy. Interestingly, Waxy enzyme activity was also influenced by the 363 aa locus in the pliable region. These results provide important theoretical regarding the high-throughput quality identification of noodle starch, functional studies, directional selection, and molecular markers of wheat Wx subunits.

Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network.

We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.

A Dynamical Systems View of Psychiatric Disorders-Theory: A Review.

Importance: Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations: In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance: Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review.

Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations: Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance: Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).