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Background: Assessing short-term growth in humans is still fraught with difficulties. Especially when looking for small variations and increments, such as mini growth spurts, high precision instruments or frequent measurements are necessary. Daily measurements however require a lot of effort, both for anthropologists and for the subjects. Therefore, new sophisticated approaches are needed that reduce fluctuations and reveal underlying patterns.
Objectives: Changepoints are abrupt variations in the properties of time series data. In the context of growth, such variations could be variation in mean height. By adjusting the variance and using different growth models, we assessed the ability of changepoint analysis to analyse short-term growth and detect mini growth spurts.
Sample and Methods: We performed Bayesian changepoint analysis on simulated growth data using the bcp package in R. Simulated growth patterns included stasis, linear growth, catch-up growth, and mini growth spurts. Specificity and a normalised variant of the Matthews correlation coefficient (MCC) were used to assess the algorithm’s performance. Welch’s t-test was used to compare differences of the mean.
Results: First results show that changepoint analysis can detect mini growth spurts. However, the ability to detect mini growth spurts is highly dependent on measurement error. Data preparation, such as ranking and rotating time series data, showed negligible improvements. Missing data was an issue and may affect the prediction quality of the classification metrics.
Conclusion: Changepoint analysis is a promising tool to analyse short-term growth. However, further optimisation and analysis of real growth data is needed to make broader generalisations.
Human growth data analysis and statistics – the 5th Gülpe International Student Summer School
(2023)
The Summer School in Gülpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year’s focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School’s emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in Gülpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.
Twenty-four scientists met for the annual Auxological conference held at Krobielowice castle, Poland, to discuss the diverse influences of the environment and of social behavior on growth following last year’s focus on growth and public health concerns (Hermanussen et al., 2022b). Growth and final body size exhibit marked plastic responses to ecological conditions. Among the shortest are the pygmoid people of Rampasasa, Flores, Indonesia, who still live under most secluded insular conditions. Genetics and nutrition are usually considered responsible for the poor growth in many parts of this world, but evidence is accumulating on the prominent impact of social embedding on child growth. Secular trends not only in the growth of height, but also in body proportions, accompany the secular changes in the social, economic and political conditions, with major influences on the emotional and educational circumstances under which the children grow up (Bogin, 2021). Aspects of developmental tempo and aspects of sports were discussed, and the impact of migration by the example of women from Bangladesh who grew up in the UK. Child growth was considered in particular from the point of view of strategic adjustments of individual size within the network of its social group. Theoretical considerations on network characteristics were presented and related to the evolutionary conservation of growth regulating hypothalamic neuropeptides that have been shown to link behavior and physical growth in the vertebrate species. New statistical approaches were presented for the evaluation of short term growth measurements that permit monitoring child growth at intervals of a few days and weeks.
The knowledge of transformation pathways and identification of transformation products (TPs) of veterinary drugs is important for animal health, food, and environmental matters. The active agent Monensin (MON) belongs to the ionophore antibiotics and is widely used as a veterinary drug against coccidiosis in broiler farming. However, no electrochemically (EC) generated TPs of MON have been described so far. In this study, the online coupling of EC and mass spectrometry (MS) was used for the generation of oxidative TPs. EC-conditions were optimized with respect to working electrode material, solvent, modifier, and potential polarity. Subsequent LC/HRMS (liquid+ chromatography/high resolution mass spectrometry) and MS/MS experiments were performed to identify the structures of derived TPs by a suspected target analysis. The obtained EC-results were compared to TPs observed in metabolism tests with microsomes and hydrolysis experiments of MON. Five previously undescribed TPs of MON were identified in our EC/MS based study and one TP, which was already known from literature and found by a microsomal assay, could be confirmed. Two and three further TPs were found as products in microsomal tests and following hydrolysis, respectively. We found decarboxylation, O-demethylation and acid-catalyzed ring-opening reactions to be the major mechanisms of MON transformation
Dermal Delivery of the High-Molecular-Weight Drug Tacrolimus by Means of Polyglycerol-Based Nanogels
(2019)
Polyglycerol-based thermoresponsive nanogels (tNGs) have been shown to have excellent skin hydration properties and to be valuable delivery systems for sustained release of drugs into skin. In this study, we compared the skin penetration of tacrolimus formulated in tNGs with a commercial 0.1% tacrolimus ointment. The penetration of the drug was investigated in ex vivo abdominal and breast skin, while different methods for skin barrier disruption were investigated to improve skin permeability or simulate inflammatory conditions with compromised skin barrier. The amount of penetrated tacrolimus was measured in skin extracts by liquid chromatography tandem-mass spectrometry (LC-MS/MS), whereas the inflammatory markers IL-6 and IL-8 were detected by enzyme-linked immunosorbent assay (ELISA). Higher amounts of tacrolimus penetrated in breast as compared to abdominal skin or in barrier-disrupted as compared to intact skin, confirming that the stratum corneum is the main barrier for tacrolimus skin penetration. The anti-proliferative effect of the penetrated drug was measured in skin tissue/Jurkat cells co-cultures. Interestingly, tNGs exhibited similar anti-proliferative effects as the 0.1% tacrolimus ointment. We conclude that polyglycerol-based nanogels represent an interesting alternative to paraffin-based formulations for the treatment of inflammatory skin conditions.
In this paper, we investigate the continuous version of modified iterative Runge–Kutta-type methods for nonlinear inverse ill-posed problems proposed in a previous work. The convergence analysis is proved under the tangential cone condition, a modified discrepancy principle, i.e., the stopping time T is a solution of ∥𝐹(𝑥𝛿(𝑇))−𝑦𝛿∥=𝜏𝛿+ for some 𝛿+>𝛿, and an appropriate source condition. We yield the optimal rate of convergence.
In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)(CDOM)) and absorption slope (S300-500) in lakes using field sampling and optical remote sensing data for an area of 350 km(2) in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a()(CDOM) data from 18 lakes sampled in the field to 356 lakes in the study area (model R-2 = 0.79). Values of a(440)(CDOM) in 356 lakes varied from 0.48 to 8.35 m(-1) with a median of 1.43 m(-1). This a()(CDOM) dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R-2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)(CDOM) = 5.3 m(-1)). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)(CDOM) = 3.8 m(-1)) compared to lakes located on higher terraces.
The light reactions of photosynthesis are carried out by a series of multiprotein complexes embedded in thylakoid membranes. Among them, photosystem I (PSI), acting as plastocyanin-ferderoxin oxidoreductase, catalyzes the final reaction. Together with light-harvesting antenna I, PSI forms a high-molecular-weight supercomplex of ~600 kDa, consisting of eighteen subunits and nearly two hundred co-factors. Assembly of the various components into a functional thylakoid membrane complex requires precise coordination, which is provided by the assembly machinery. Although this includes a small number of proteins (PSI assembly factors) that have been shown to play a role in the formation of PSI, the process as a whole, as well as the intricacy of its members, remains largely unexplored.
In the present work, two approaches were used to find candidate PSI assembly factors. First, EnsembleNet was used to select proteins thought to be functionally related to known PSI assembly factors in Arabidopsis thaliana (approach I), and second, co-immunoprecipitation (Co-IP) of tagged PSI assembly factors in Nicotiana tabacum was performed (approach II).
Here, the novel PSI assembly factors designated CO-EXPRESSED WITH PSI ASSEMBLY 1 (CEPA1) and Ycf4-INTERACTING PROTEIN 1 (Y4IP1) were identified. A. thaliana null mutants for CEPA1 and Y4IP1 showed a growth phenotype and pale leaves compared with the wild type. Biophysical experiments using pulse amplitude modulation (PAM) revealed insufficient electron transport on the PSII acceptor side. Biochemical analyses revealed that both CEPA1 and Y4IP1 are specifically involved in PSI accumulation in A. thaliana at the post-translational level but are not essential. Consistent with their roles as factors in the assembly of a thylakoid membrane protein complex, the two proteins localize to thylakoid membranes. Remarkably, cepa1 y4ip1 double mutants exhibited lethal phenotypes in early developmental stages under photoautotrophic growth. Finally, co-IP and native gel experiments supported a possible role for CEPA1 and Y4IP1 in mediating PSI assembly in conjunction with other PSI assembly factors (e.g., PPD1- and PSA3-CEPA1 and Ycf4-Y4IP1). The fact that CEPA1 and Y4IP1 are found exclusively in green algae and higher plants suggests eukaryote-specific functions. Although the specific mechanisms need further investigation, CEPA1 and Y4IP1 are two novel assembly factors that contribute to PSI formation.
We investigate whether political ideology has an observable effect on decarbonization ambition, renewable power aims, and preferences for power system balancing technologies in four European countries. Based on the Energy Logics framework, we identify ideologically different transition strategies (state-centered, market-centered, grassroots-centered) contained in government policies and opposition party programs valid in 2019. We compare these policies and programs with citizen poll data. We find that ideology has a small effect: governments and political parties across the spectrum have similar, and relatively ambitious, decarbonization and renewables targets. This mirrors citizens' strong support for ambitious action regardless of their ideological self-description. However, whereas political positions on phasing out fossil fuel power are clear across the policy space, positions on phasing in new flexibility options to balance intermittent renewables are vague or non-existent. As parties and citizens agree on strong climate and renewable power aims, the policy ambition is likely to remain high, even if governments change.
Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers
(2009)
A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers
(2009)
A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
In recent decades, astronomy has seen a boom in large-scale stellar surveys of the Galaxy. The detailed information obtained about millions of individual stars in the Milky Way is bringing us a step closer to answering one of the most outstanding questions in astrophysics: how do galaxies form and evolve? The Milky Way is the only galaxy where we can dissect many stars into their high-dimensional chemical composition and complete phase space, which analogously as fossil records can unveil the past history of the genesis of the Galaxy. The processes that lead to large structure formation, such as the Milky Way, are critical for constraining cosmological models; we call this line of study Galactic archaeology or near-field cosmology.
At the core of this work, we present a collection of efforts to chemically and dynamically characterise the disks and bulge of our Galaxy. The results we present in this thesis have only been possible thanks to the advent of the Gaia astrometric satellite, which has revolutionised the field of Galactic archaeology by precisely measuring the positions, parallax distances and motions of more than a billion stars. Another, though not less important, breakthrough is the APOGEE survey, which has observed spectra in the near-infrared peering into the dusty regions of the Galaxy, allowing us to determine detailed chemical abundance patterns in hundreds of thousands of stars. To accurately depict the Milky Way structure, we use and develop the Bayesian isochrone fitting tool/code called StarHorse; this software can predict stellar distances, extinctions and ages by combining astrometry, photometry and spectroscopy based on stellar evolutionary models. The StarHorse code is pivotal to calculating distances where Gaia parallaxes alone cannot allow accurate estimates.
We show that by combining Gaia, APOGEE, photometric surveys and using StarHorse, we can produce a chemical cartography of the Milky way disks from their outermost to innermost parts. Such a map is unprecedented in the inner Galaxy. It reveals a continuity of the bimodal chemical pattern previously detected in the solar neighbourhood, indicating two populations with distinct formation histories. Furthermore, the data reveals a chemical gradient within the thin disk where the content of 𝛼-process elements and metals is higher towards the centre. Focusing on a sample in the inner MW we confirm the extension of the chemical duality to the innermost regions of the Galaxy. We find stars with bar shape orbits to show both high- and low-𝛼 abundances, suggesting the bar formed by secular evolution trapping stars that already existed. By analysing the chemical orbital space of the inner Galactic regions, we disentangle the multiple populations that inhabit this complex region. We reveal the presence of the thin disk, thick disk, bar, and a counter-rotating population, which resembles the outcome of a perturbed proto-Galactic disk. Our study also finds that the inner Galaxy holds a high quantity of super metal-rich stars up to three times solar suggesting it is a possible repository of old super-metal-rich stars found in the solar neighbourhood.
We also enter into the complicated task of deriving individual stellar ages. With StarHorse, we calculate the ages of main-sequence turn-off and sub-giant stars for several public spectroscopic surveys. We validate our results by investigating linear relations between chemical abundances and time since the 𝛼 and neutron capture elements are sensitive to age as a reflection of the different enrichment timescales of these elements. For further study of the disks in the solar neighbourhood, we use an unsupervised machine learning algorithm to delineate a multidimensional separation of chrono-chemical stellar groups revealing the chemical thick disk, the thin disk, and young 𝛼-rich stars. The thick disk is shown to have a small age dispersion indicating its fast formation contrary to the thin disk that spans a wide range of ages.
With groundbreaking data, this thesis encloses a detailed chemo-dynamical view of the disk and bulge of our Galaxy. Our findings on the Milky Way can be linked to the evolution of high redshift disk galaxies, helping to solve the conundrum of galaxy formation.
Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
Background
The association between bivariate variables may not necessarily be homogeneous throughout the whole range of the variables. We present a new technique to describe inhomogeneity in the association of bivariate variables.
Methods
We consider the correlation of two normally distributed random variables. The 45° diagonal through the origin of coordinates represents the line on which all points would lie if the two variables completely agreed. If the two variables do not completely agree, the points will scatter on both sides of the diagonal and form a cloud. In case of a high association between the variables, the band width of this cloud will be narrow, in case of a low association, the band width will be wide. The band width directly relates to the magnitude of the correlation coefficient. We then determine the Euclidean distances between the diagonal and each point of the bivariate correlation, and rotate the coordinate system clockwise by 45°. The standard deviation of all Euclidean distances, named “global standard deviation”, reflects the band width of all points along the former diagonal. Calculating moving averages of the standard deviation along the former diagonal results in “locally structured standard deviations” and reflect patterns of “locally structured correlations (LSC)”. LSC highlight inhomogeneity of bivariate correlations. We exemplify this technique by analyzing the association between body mass index (BMI) and hip circumference (HC) in 6313 healthy East German adults aged 18 to 70 years.
Results
The correlation between BMI and HC in healthy adults is not homogeneous. LSC is able to identify regions where the predictive power of the bivariate correlation between BMI and HC increases or decreases, and highlights in our example that slim people have a higher association between BMI and HC than obese people.
Conclusion
Locally structured correlations (LSC) identify regions of higher or lower than average correlation between two normally distributed variables.
Physical activity and exercise are effective approaches in prevention and therapy of multiple diseases. Although the specific characteristics of lengthening contractions have the potential to be beneficial in many clinical conditions, eccentric training is not commonly used in clinical populations with metabolic, orthopaedic, or neurologic conditions. The purpose of this pilot study is to investigate the feasibility, functional benefits, and systemic responses of an eccentric exercise program focused on the trunk and lower extremities in people with low back pain (LBP) and multiple sclerosis (MS). A six-week eccentric training program with three weekly sessions is performed by people with LBP and MS. The program consists of ten exercises addressing strength of the trunk and lower extremities. The study follows a four-group design (N = 12 per group) in two study centers (Israel and Germany): three groups perform the eccentric training program: A) control group (healthy, asymptomatic); B) people with LBP; C) people with MS; group D (people with MS) receives standard care physiotherapy. Baseline measurements are conducted before first training, post-measurement takes place after the last session both comprise blood sampling, self-reported questionnaires, mobility, balance, and strength testing. The feasibility of the eccentric training program will be evaluated using quantitative and qualitative measures related to the study process, compliance and adherence, safety, and overall program assessment. For preliminary assessment of potential intervention effects, surrogate parameters related to mobility, postural control, muscle strength and systemic effects are assessed. The presented study will add knowledge regarding safety, feasibility, and initial effects of eccentric training in people with orthopaedic and neurological conditions. The simple exercises, that are easily modifiable in complexity and intensity, are likely beneficial to other populations. Thus, multiple applications and implementation pathways for the herein presented training program are conceivable.
Recent research suggests that design thinking practices may foster the development of needed capabilities in new digitalised landscapes. However, existing publications represent individual contributions, and we lack a holistic understanding of the value of design thinking in a digital world. No review, to date, has offered a holistic retrospection of this research. In response, in this bibliometric review, we aim to shed light on the intellectual structure of multidisciplinary design thinking literature related to capabilities relevant to the digital world in higher education and business settings, highlight current trends and suggest further studies to advance theoretical and empirical underpinnings. Our study addresses this aim using bibliometric methods—bibliographic coupling and co-word analysis as they are particularly suitable for identifying current trends and future research priorities at the forefront of the research. Overall, bibliometric analyses of the publications dealing with the related topics published in the last 10 years (extracted from the Web of Science database) expose six trends and two possible future research developments highlighting the expanding scope of the design thinking scientific field related to capabilities required for the (more sustainable and human-centric) digital world. Relatedly, design thinking becomes a relevant approach to be included in higher education curricula and human resources training to prepare students and workers for the changing work demands. This paper is well-suited for education and business practitioners seeking to embed design thinking capabilities in their curricula and for design thinking and other scholars wanting to understand the field and possible directions for future research.
Inverted perovskite solar cells still suffer from significant non-radiative recombination losses at the perovskite surface and across the perovskite/C₆₀ interface, limiting the future development of perovskite-based single- and multi-junction photovoltaics. Therefore, more effective inter- or transport layers are urgently required. To tackle these recombination losses, we introduce ortho-carborane as an interlayer material that has a spherical molecular structure and a three-dimensional aromaticity. Based on a variety of experimental techniques, we show that ortho-carborane decorated with phenylamino groups effectively passivates the perovskite surface and essentially eliminates the non-radiative recombination loss across the perovskite/C₆₀ interface with high thermal stability. We further demonstrate the potential of carborane as an electron transport material, facilitating electron extraction while blocking holes from the interface. The resulting inverted perovskite solar cells deliver a power conversion efficiency of over 23% with a low non-radiative voltage loss of 110 mV, and retain >97% of the initial efficiency after 400 h of maximum power point tracking. Overall, the designed carborane based interlayer simultaneously enables passivation, electron-transport and hole-blocking and paves the way toward more efficient and stable perovskite solar cells.
Inverted perovskite solar cells still suffer from significant non-radiative recombination losses at the perovskite surface and across the perovskite/C₆₀ interface, limiting the future development of perovskite-based single- and multi-junction photovoltaics. Therefore, more effective inter- or transport layers are urgently required. To tackle these recombination losses, we introduce ortho-carborane as an interlayer material that has a spherical molecular structure and a three-dimensional aromaticity. Based on a variety of experimental techniques, we show that ortho-carborane decorated with phenylamino groups effectively passivates the perovskite surface and essentially eliminates the non-radiative recombination loss across the perovskite/C₆₀ interface with high thermal stability. We further demonstrate the potential of carborane as an electron transport material, facilitating electron extraction while blocking holes from the interface. The resulting inverted perovskite solar cells deliver a power conversion efficiency of over 23% with a low non-radiative voltage loss of 110 mV, and retain >97% of the initial efficiency after 400 h of maximum power point tracking. Overall, the designed carborane based interlayer simultaneously enables passivation, electron-transport and hole-blocking and paves the way toward more efficient and stable perovskite solar cells.
Achilles tendinopathy (AT) is a debilitating injury in athletes, especially for those engaged in repetitive stretch-shortening cycle activities. Clinical risk factors are numerous, but it has been suggested that altered biomechanics might be associated with AT. No systematic review has been conducted investigating these biomechanical alterations in specifically athletic populations. Therefore, the aim of this systematic review was to compare the lower-limb biomechanics of athletes with AT to athletically matched asymptomatic controls. Databases were searched for relevant studies investigating biomechanics during gait activities and other motor tasks such as hopping, isolated strength tasks, and reflex responses. Inclusion criteria for studies were an AT diagnosis in at least one group, cross-sectional or prospective data, at least one outcome comparing biomechanical data between an AT and healthy group, and athletic populations. Studies were excluded if patients had Achilles tendon rupture/surgery, participants reported injuries other than AT, and when only within-subject data was available.. Effect sizes (Cohen's d) with 95% confidence intervals were calculated for relevant outcomes. The initial search yielded 4,442 studies. After screening, twenty studies (775 total participants) were synthesised, reporting on a wide range of biomechanical outcomes. Females were under-represented and patients in the AT group were three years older on average. Biomechanical alterations were identified in some studies during running, hopping, jumping, strength tasks and reflex activity. Equally, several biomechanical variables studied were not associated with AT in included studies, indicating a conflicting picture. Kinematics in AT patients appeared to be altered in the lower limb, potentially indicating a pattern of “medial collapse”. Muscular activity of the calf and hips was different between groups, whereby AT patients exhibited greater calf electromyographic amplitudes despite lower plantar flexor strength. Overall, dynamic maximal strength of the plantar flexors, and isometric strength of the hips might be reduced in the AT group. This systematic review reports on several biomechanical alterations in athletes with AT. With further research, these factors could potentially form treatment targets for clinicians, although clinical approaches should take other contributing health factors into account. The studies included were of low quality, and currently no solid conclusions can be drawn.
Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.