Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance

Artificial intelligence (AI) can accelerate the design of materials by identifying correlations and complex patterns in data. However, AI methods commonly attempt to describe the entire, immense materials space with a single model, while it is typical that different mechanisms govern the materials behaviors across the materials space. The subgroup-discovery (SGD) approach identifies local rules describing exceptional subsets of data with respect to a given target. Thus, SGD can focus on mechanisms leading to exceptional performance. However, the identification of appropriate SG rules requires a careful consideration of the generality-exceptionality tradeoff. Here, we discuss challenges to advance the SGD approach in materials science and analyse the tradeoff between exceptionality and generality based on a Pareto front of SGD solutions

Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification

Autism spectrum disorder (ASD) is a prevalent psychiatric condition characterized by atypical cognitive, emotional, and social patterns. Timely and accurate diagnosis is crucial for effective interventions and improved outcomes in individuals with ASD. In this study, we propose a novel Multi-Atlas Enhanced Transformer framework, METAFormer, ASD classification. Our framework utilizes resting-state functional magnetic resonance imaging data from the ABIDE I dataset, comprising 406 ASD and 476 typical control (TC) subjects. METAFormer employs a multi-atlas approach, where flattened connectivity matrices from the AAL, CC200, and DOS160 atlases serve as input to the transformer encoder. Notably, we demonstrate that self-supervised pretraining, involving the reconstruction of masked values from the input, significantly enhances classification performance without the need for additional or separate training data. Through stratified cross-validation, we evaluate the proposed framework and show that it surpasses state-of-the-art performance on the ABIDE I dataset, with an average accuracy of 83.7% and an AUC-score of 0.832. The code for our framework is available at https://github.com/Lugges991/METAForme

Dry-jet wet spinning of thermally stable lignin-textile grade polyacrylonitrile fibers regenerated from chloride-based ionic liquids compounds

In this paper, we report on the use of amorphous lignin, a waste by-product of the paper industry, for the production of high performance carbon fibers (CF) as precursor with improved thermal stability and thermo-mechanical properties. The precursor was prepared by blending of lignin with polyacrylonitrile (PAN), which was previously dissolved in an ionic liquid. The fibers thus produced offered very high thermal stability as compared with the fiber consisting of pure PAN. The molecular compatibility, miscibility, and thermal stability of the system were studied by means of shear rheological measurements. The achieved mechanical properties were found to be related to the temperature-dependent relaxation time (consistence parameter) of the spinning dope and the diffusion kinetics of the ionic liquids from the fibers into the coagulation bath. Furthermore, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and dynamic mechanical tests (DMA) were utilized to understand in-depth the thermal and the stabilization kinetics of the developed fibers and the impact of lignin on the stabilization process of the fibers. Low molecular weight lignin increased the thermally induced physical shrinkage, suggesting disturbing effects on the semi-crystalline domains of the PAN matrix, and suppressed the chemically induced shrinkage of the fibers. The knowledge gained throughout the present paper allows summarizing a novel avenue to develop lignin-based CF designed with adjusted thermal stability

Widely Used Commercial ELISA Does Not Detect Precursor of Haptoglobin2, but Recognizes Properdin as a Potential Second Member of the Zonulin Family

Background: There is increasing evidence for the role of impaired intestinal permeability in obesity and associated metabolic diseases. Zonulin is an established serum marker for intestinal permeability and identical to pre-haptoglobin2. Here, we aimed to investigate the relationship between circulating zonulin and metabolic traits related to obesity. Methods: Serum zonulin was measured by using a widely used commercial ELISA kit in 376 subjects from the metabolically well-characterized cohort of Sorbs from Germany. In addition, haptoglobin genotype was determined in DNA samples from all study subjects. Results: As zonulin concentrations did not correlate to the haptoglobin genotypes, we investigated the specificity of the zonulin ELISA assay using antibody capture experiments, mass spectrometry, and Western blot analysis. Using serum samples that gave the highest or lowest ELISA signals, we detected several proteins that are likely to be captured by the antibody in the present kit. However, none of these proteins corresponds to pre-haptoglobin2. We used increasing concentrations of recombinant pre-haptoglobin2 and complement C3 as one of the representative captured proteins and the ELISA kit did not detect either. Western blot analysis using both the polyclonal antibodies used in this kit and monoclonal antibodies rose against zonulin showed a similar protein recognition pattern but with different intensity of detection. The protein(s) measured using the ELISA kit was (were) significantly increased in patients with diabetes and obesity and correlated strongly with markers of the lipid and glucose metabolism. Combining mass spectrometry and Western blot analysis using the polyclonal antibodies used in the ELISA kit, we identified properdin as another member of the zonulin family. Conclusion: Our study suggests that the zonulin ELISA does not recognize pre-haptoglobin2, rather structural (and possibly functional) analog proteins belonging to the mannose-associated serine protease family, with properdin being the most likely possible candidate

Climate and vegetational regime shifts in the late Paleozoic ice age earth

The late Paleozoic earth experienced alternation between glacial and non-glacial climates at multiple temporal scales, accompanied by atmospheric CO 2 fluctuations and global warming intervals, often attended by significant vegetational changes in equatorial latitudes of Pangaea. We assess the nature of climate–vegetation interaction during two time intervals: middle–late Pennsylvanian transition and Pennsylvanian–Permian transition, each marked by tropical warming and drying. In case study 1, there is a catastrophic intra-biomic reorganization of dominance and diversity in wetland, evergreen vegetation growing under humid climates. This represents a threshold-type change, possibly a regime shift to an alternative stable state. Case study 2 is an inter-biome dominance change in western and central Pangaea from humid wetland and seasonally dry to semi-arid vegetation. Shifts between these vegetation types had been occurring in Euramerican portions of the equatorial region throughout the late middle and late Pennsylvanian, the drier vegetation reaching persistent dominance by Early Permian. The oscillatory transition between humid and seasonally dry vegetation appears to demonstrate a threshold-like behavior but probably not repeated transitions between alternative stable states. Rather, changes in dominance in lowland equatorial regions were driven by long-term, repetitive climatic oscillations, occurring with increasing intensity, within overall shift to seasonal dryness through time. In neither case study are there clear biotic or abiotic warning signs of looming changes in vegetational composition or geographic distribution, nor is it clear that there are specific, absolute values or rates of environmental change in temperature, rainfall distribution and amount, or atmospheric composition, approach to which might indicate proximity to a terrestrial biotic-change threshold.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73033/1/j.1472-4669.2009.00192.x.pd

Towards Experimental Handbooks in Catalysis

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA‐Epilepsy study

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross‐sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA‐Epilepsy working group. Methods: A state‐of‐the‐art deep learning‐based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE‐HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( η ρ max 2 $$\eta {\mathit{\mathsf{\rho}}}_{\mathsf{max}}^{\mathsf{2}}$$ = .05) and longer epilepsy duration ( η ρ max 2 $$\eta {\mathit{\mathsf{\rho}}}_{\mathsf{max}}^{\mathsf{2}}$$ = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE‐HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy