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String theory suggests the existence of a minimum length scale. An exciting quantum mechanical implication of this feature is a modification of the uncertainty principle. In contrast to the conventional approach, this generalised uncertainty principle does not allow to resolve space–time distances below the Planck length. In models with extra dimensions, which are also motivated by string theory, the Planck scale can be lowered to values accessible by ultra high energetic cosmic rays (UHECRs) and by future colliders, i.e., Mf≈ 1 TeV. It is demonstrated that in this novel scenario, short distance physics below 1/Mf is completely cloaked by the uncertainty principle. Therefore, Planckian effects could be the final physics discovery at future colliders and in UHECRs. As an application, we predict the modifications to the e+e−→f+f− cross-sections.
Die auf dem ACDM-Modell beruhenden numerischen Simulationen der gravitativen Strukturbildung sind auf Skalen M >> 10 hoch 10 M sehr erfolgreich, insbesondere konvergieren die Verfahren hinsichtlich des vorhergesagten Masseanteils der Halos an der Gesamtmasse von Galaxien. Jedoch konvergieren die Simulationen nicht bezüglich der lokalen Überdichten von CDM in den Halos, vielmehr setzt sich gravitative Strukturbildung auf immer kleinere Skalen fort. Numerisch kann keine Massen-Schwelle berechnet werden, unterhalb derer keine CDM-Strukturen mehr gravitativ gebildet werden. Die Kenntnis der lokalen Überdichten in den CDM-Wolken und die Verteilung der CDM-Wolken ist jedoch für Experimente zum direkten und indirekten Nachweis von CDM-Teilchen essentiell. Aus den lokalen Überdichten folgen für Experimente zum direkten Nachweis die einfallende Stromdichten der CDM-Teilchen und für Experimente zum indirekten Nachweis die Stromdichte der Annihilationsprodukte. Außerdem können die lokalen Überdichten als Gravitationslinsen wirken. In dieser Arbeit werden Massen Schwellen analytisch berechnet, unterhalb derer akustische Störungen in CDM nicht mehr zur gravitativen Strukturbildung beitragen können. Das Massen-Spektrum von lokalen Überdichten ist nach unten durch zwei unterschiedliche Mechanismen beschränkt: (1) Während der kinetischen Entkopplung formieren sich Nichtgleichgewichtsprozesse, die sich kollektiv als Reihungsphänomene konstituieren. Im lineare Regime sind dies die Volumenviskosität, die Scherungsviskosität und die Wärmeleitung. Die dissipativen Prozesse deponieren Energie und Impuls der akustischen Störungen in die Ebene senkrecht zur Ausbreitungsrichtung der Störungen und schmieren diese so aus. (II) Nach dem kinetischen Entkopplungsprozeß strömt CDM frei auf Geodäten. Dies ermöglicht einen Strom von Teilchen von überdichten in unterdichte Regionen, so daß die Amplituden der lokalen Überdichten weiter gedämpft werden. Die lokalen Transportkoeffizienten in (1) werden durch einen legitimen Vergleich von hydrodynamischer und kinetischer Beschreibung schwach dissipativer Prozesse gewonnen. Dissipative Prozesse induzieren eine Dämpfungsmasse Mc ungefähr gleich 10 hoch minus 9 M in SUSY-CDM und beschränken damit das Spektrum akustischer Störungen in SUSY-CDM. Freies Strömen (II) von CDM-Teilchen auf Geodäten induziert eine weitere Dämpfungsmasse M fs ungefähr gleich 10 hoch minus 6 M in SUSY-CDM, wobei das berechnete M d als Anfangswert dient. Die berechneten Schwellen liefern konsistente Schranken für numerische Simulationen, die weit unterhalb des momentanen numerischen Auflösungsvermögens liegen. Weiterhin folgt aus den Schwellen die Masse der ersten rein gravitativ gebundenen CDM-Wolken. Aus diesen bilden sich im Rahmen der hierarchischen Strukturbildung größere Substrukturen bis hin zu den heute vorhandenen CDM-Halos.
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
