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Altera Roma : art and empire from Mâerida to Mâexico
\"Altera Roma explores the confrontation of two cultures--European and Amerindian--and two empires--Spanish and Aztec. In an age of exploration and conquest, Spanish soldiers, missionaries, and merchants brought an array of cultural preconceptions. Their encounter with Aztec civilization coincided with Europe's rediscovery of classical antiquity, and Tenochtitlâan came to be regarded a 'second Rome, ' altera Roma. Iberia's past as the Roman province of Hispania served to both guide and critique the Spanish overseas mission. The dialogue that emerged between the Old World and the New World shaped a dual heritage into the unique culture of Nueva Espaنna. In this volume, 10 eminent historians and archaeologists examine the analogies between empires widely separated in time and place, and consider how monumental art and architecture created 'theater states, ' a strategy that links ancient Rome, Hapsburg Spain, preconquest Mexico, and other imperial regimes\"--Provided by publisher.
Book
Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at
https://github.com/qingyuzhao/br-net
.
The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.
Journal Article
Downscaling air temperatures for high-resolution niche modeling in a valley of the Amazon lowland forests: A case study on the microclima R package
The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond.
Journal Article
Disentangling common and specific neural subprocesses of response inhibition
Response inhibition is disturbed in several disorders sharing impulse control deficits as a core symptom. Since response inhibition is a cognitively and neurally multifaceted function which has been shown to rely on differing neural subprocesses and neurotransmitter systems, further differentiation to define neurophysiological endophenotypes is essential. Response inhibition may involve at least three separable cognitive subcomponents, i.e. interference inhibition, action withholding, and action cancelation. Here, we introduce a novel paradigm – the Hybrid Response Inhibition task – to disentangle interference inhibition, action withholding and action cancelation and their neural subprocesses within one task setting during functional magnetic resonance imaging (fMRI). To validate the novel task, results were compared to a battery of separate, standard response inhibition tasks independently capturing these subcomponents and subprocesses. Across all subcomponents, mutual activation was present in the right inferior frontal cortex (rIFC), pre-supplementary motor area (pre-SMA) and parietal regions. Interference inhibition revealed stronger activation in pre-motor and parietal regions. Action cancelation resulted in stronger activation in fronto-striatal regions. Our results show that all subcomponents share a common neural network and thus all constitute different subprocesses of response inhibition. Subprocesses, however, differ to the degree of regional involvement: interference inhibition relies more pronouncedly on a fronto-parietal–pre-motor network suggesting its close relation to response selection processes. Action cancelation, in turn, is more strongly associated with the fronto-striatal pathway implicating it as a late subcomponent of response inhibition. The new paradigm reliably captures three putatively subsequent subprocesses of response inhibition and might be a promising tool to differentially assess disturbed neural networks in disorders showing impulse control deficits.
► A novel task disentangling three subcomponents of response inhibition is introduced. ► All subcomponents share a common fronto-parietal inhibition network. ► Mutual activation in the rIFC and the pre-SMA is present in all subcomponents. ► Interference inhibition is based on a fronto-parietal–pre-motor network. ► Action cancelation relies on the (indirect) prefrontal–striatal pathway.
Journal Article
Enhanced magneto-optical effects in magnetoplasmonic crystals
Plasmonics allows light to be localized on length scales much shorter than its wavelength, which makes it possible to integrate photonics and electronics on the nanoscale. Magneto-optical materials are appealing for applications in plasmonics because they open up the possibility of using external magnetic fields in plasmonic devices. Here, we fabricate a new magneto-optical material, a magnetoplasmonic crystal, that consists of a nanostructured noble-metal film on top of a ferromagnetic dielectric, and we demonstrate an enhanced Kerr effect with this material. Such magnetoplasmonic crystals could have applications in telecommunications, magnetic field sensing and all-optical magnetic data storage.
A new magneto-optical material consisting of a nanostructured gold film on top of a ferromagnetic dielectric demonstrated significantly enhanced Faraday and Kerr effects.
Journal Article
Deep learning identifies morphological determinants of sex differences in the pre-adolescent brain
•We proposed a deep learning approach to identify sex differences in developing brain structures of pre-adolescents.•We used a large cohort of (N=8,144; age 9 and 10 years) from the Adolescent Brain Cognitive Development (ABCD) study.•The identified pattern accounted for confounding factors (head size, age, puberty development, socioeconomic status).•The pattern comprised cerebellar and subcortical structures.•Our method identified differences specific to the cerebellum to pubertal development.
The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.
Journal Article
Damage tolerant fatigue behavior of laminated metallic composites with dissimilar yield strength
Aside from other demands, damage tolerance is an important design criterion for cyclically stressed components of commercial aircraft, such as the fuselage or wings. Heterostructured materials, such as laminated metal composites (LMCs) produced by the accumulative roll bonding process (ARB), can be tailored to provide high resistance against fatigue crack growth by utilizing material heterogeneities at interfaces. In this study, the influence of the layer thickness and dissimilar yield strength at interfaces on the fatigue crack growth behavior in LMCs is investigated systematically to derive design criteria for highly damage tolerant laminated composites. A linear rule of mixture behavior is introduced as a benchmark for the damage tolerant behavior of the laminated composites. The crack growth rates of the laminated composites at elevated stress intensity ranges are significantly reduced compared to both the rule of mixture concept and the behavior of the monolithic constituents. This is explained by the onset of toughening mechanisms at the vicinity of interfaces and the formation of complex crack networks. The extent of crack growth rate reduction due to toughening mechanisms depends on both the yield strength ratio as well as layer thickness of the laminated composites. A comprehensive understanding of the mechanisms responsible for the damage tolerant behavior was provided by determining the size of the plastic zone ahead of the crack tip using finite element analysis. An addition to the Paris crack-growth law was suggested, accounting for the additional influencing factors to accurately describe the significantly improved fatigue crack growth behavior of laminated composites.
Journal Article
Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation
Machine learning (ML) has significantly transformed biomedical research, leading to a growing interest in model development to advance classification accuracy in various clinical applications. However, this progress raises essential questions regarding how to rigorously compare the accuracy of different ML models. In this study, we highlight the practical challenges in quantifying the statistical significance of accuracy differences between two neuroimaging-based classification models when cross-validation (CV) is performed. Specifically, we propose an unbiased framework to assess the impact of CV setups (e.g., the number of folds) on the statistical significance. We apply this framework to three publicly available neuroimaging datasets to re-emphasize known flaws in current computation of
p
-values for comparing model accuracies. We further demonstrate that the likelihood of detecting significant differences among models varies substantially with the intrinsic properties of the data, testing procedures, and CV configurations of choice. Given that many of the above factors do not typically fall into the evaluation criteria of ML-based biomedical studies, we argue that such variability can potentially lead to
p
-hacking and inconsistent conclusions on model improvement. The obtained results from this study underscore that more rigorous practices in model comparison are urgently needed in order to mitigate the reproducibility crisis in biomedical ML research.
Journal Article
Cardiac desmosomal adhesion relies on ideal-, slip- and catch bonds
The cardiac muscle consists of individual cardiomyocytes that are mechanically linked by desmosomes. Desmosomal adhesion is mediated by densely packed and organized cadherins which, in presence of Ca
2+
, stretch out their extracellular domains (EC) and dimerize with opposing binding partners by exchanging an N-terminal tryptophan. The strand-swap binding motif of cardiac cadherins like desmocollin 2 (Dsc2) (and desmoglein2 alike) is highly specific but of low affinity with average bond lifetimes in the range of approximately 0.3 s. Notably, despite this comparatively weak interaction, desmosomes mediate a stable, tensile-resistant bond. In addition, force mediated dissociation of strand-swap dimers exhibit a reduced bond lifetime as external forces increase (slip bond). Using atomic force microscopy based single molecule force spectroscopy (AFM-SMFS), we demonstrate that Dsc2 has two further binding modes that, in addition to strand-swap dimers, most likely play a significant role in the integrity of the cardiac muscle. At short interaction times, the Dsc2 monomers associate only loosely, as can be seen from short-lived force-independent bonds. These ideal bonds are a precursor state and probably stabilize the formation of the self-inhibiting strand-swap dimer. The addition of tryptophan in the measurement buffer acts as a competitive inhibitor, preventing the N-terminal strand exchange. Here, Dsc2 dimerizes as X-dimer which clearly shows a tri-phasic slip-catch-slip type of dissociation. Within the force-mediated transition (catch) regime, Dsc2 dimers switch between a rather brittle low force and a strengthened high force adhesion state. As a result, we can assume that desmosomal adhesion is mediated not only by strand-swap dimers (slip) but also by their precursor states (ideal bond) and force-activated X-dimers (catch bond).
Journal Article
Repetitive locomotor training and physiotherapy improve walking and basic activities of daily living after stroke: a single-blind, randomized multicentre trial (DEutsche GAngtrainerStudie, DEGAS)
Objective: To evaluate the effect of repetitive locomotor training on an electromechanical gait trainer plus physiotherapy in subacute stroke patients.
Design: Randomized controlled trial.
Setting: Four German neurological rehabilitation centres. Subjects: One hundred and fifty-five non-ambulatory patients (first-time stroke <60 days).
Intervention: Group A received 20 min locomotor training and 25 min physiotherapy; group B had 45 min physiotherapy every week day for four weeks.
Main outcome measures: Primary variables were gait ability (Functional Ambulation Category, 0-5) and the Barthel Index (0-100), blindly assessed at study onset, end, and six months later for follow-up. Responders to the therapy had to become ambulatory (Functional Ambulation Category 4 or 5) or reach a Barthel Index of ≥ 75. Secondary variables were walking velocity, endurance, mobility and leg power.
Results: The intention-to-treat analysis revealed that significantly greater number of patients in group A could walk independently: 41 of 77 versus 17 of 78 in group B (P B < 0.0001) at treatment end. Also, significantly more group A patients had reached a Barthel Index ≥ 75: 44 of 77 versus 21 of 78 (P B < 0.0001). At six-month follow-up, the superior gait ability in group A persisted (54 of 77 versus 28 of 78, P B < 0.0001), while the Barthel Index responder rate did not differ. For all secondary variables, group A patients had improved significantly more (P B < 0.0001) during the treatment period, but not during follow-up.
Conclusions: Intensive locomotor training plus physiotherapy resulted in a significantly better gait ability and daily living competence in subacute stroke patients compared with physiotherapy alone.
Journal Article