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We give a collection of 16 examples which show that compositions \(g\) \(\circ\) \(f\) of well-behaved functions \(f\) and \(g\) can be badly behaved. Remarkably, in 10 of the 16 examples it suffices to take as outer function \(g\) simply a power-type or characteristic function. Such a collection of examples may serve as a source of exercises for a calculus course.
Seltene Erkrankungen (SE) werden durch die im deutschen Gesundheitssystem verwendete Diagnosenklassifikation ICD-10-GM (International Statistical Classification of Diseases and Related Health problems, 10th Revision, German Modification) nur zu einem kleinen Teil eindeutig erfasst. Daher sind Aussagen zur Häufigkeit von SE sowie zum speziellen Versorgungs- und Finanzierungsbedarf nicht möglich, was zu einer lückenhaften Datenlage als Entscheidungsgrundlage für Krankenkassen, Leistungserbringer und Gesundheitspolitik führt. Das Fehlen exakter Informationen behindert auch die wissenschaftliche Arbeit. Daher wird deutschlandweit ab 2023 die Verwendung der Alpha-ID-SE-Datei und der ORPHAcodes für die spezifische Erfassung von SE bei stationären Fällen verpflichtend.
Die Alpha-ID-SE-Datei verknüpft die ICD-10-GM-Kodes mit den international anerkannten ORPHAcodes für die Diagnose von SE. Kommerzielle Anbieter stellen zunehmend die benötigten IT-Tools zur Kodierung von SE zur Verfügung. An mehreren Universitätskliniken mit Zentren für SE wurden Lösungen etabliert, die eine vollständige Kodierung gewährleisten sollen. Hierzu gehören finanzielle Anreize für die kodierenden Bereiche, konkrete Nachfragen nach dem Vorliegen einer SE beim Kodiervorgang und eine semiautomatische Kodierung bei Patient*innen, die schon einmal mit einer SE an der Einrichtung betreut worden waren. Eine Kombination der verschiedenen Ansätze verspricht die höchste Wahrscheinlichkeit einer vollständigen Kodierung.
Für ein umfängliches Bild der SE im Gesundheitssystem und um dem speziellen Versorgungs- und Finanzierungsbedarf besser Rechnung tragen zu können, wäre auch im ambulanten Bereich eine möglichst spezifische und eindeutige Kodierung wünschenswert. Für komplexe SE und bisher undiagnostizierte Patient*innen wird zusätzlich eine strukturierte Erfassung des Phänotyps benötigt.
Hyper-IgM syndrome type 2 (HIGM2) is a B cell intrinsic primary immunodeficiency caused by mutations in AICDA encoding activation-induced cytidine deaminase (AID) which impair immunoglobulin class switch recombination (CSR) and somatic hypermutation (SHM). Whereas autosomal-recessive AID-deficiency (AR-AID) affects both CSR and SHM, the autosomal-dominant form (AD-AID) due to C-terminal heterozygous variants completely abolishes CSR but only partially affects SHM. AR-AID patients display enhanced germinal center (GC) reactions and autoimmune manifestations, which are not present in AD-AID, suggesting that SHM but not CSR regulates GC reactions and peripheral B cell tolerance. Herein, we describe two siblings with HIGM2 due to a novel homozygous AICDA mutation (c.428-1G > T) which disrupts the splice acceptor site of exon 4 and results in the sole expression of a truncated AID variant that lacks 10 highly conserved amino acids encoded by exon 4 (AID-ΔE4a). AID-ΔE4a patients suffered from defective CSR and enhanced GC reactions and were therefore indistinguishable from other AR-AID patients. However, the AID-ΔE4a variant only partially affected SHM as observed in AD-AID patients. In addition, AID-ΔE4a but not AD-AID patients revealed impaired targeting of mutational hotspot motives and distorted mutational patterns. Hence, qualitative defects in AID function and altered SHM rather than global decreased SHM activity may account for the disease phenotype in these patients.
Understanding the causal relationship between genotype and phenotype is a major objective in biology. Genome-wide association studies (GWAS) correlate genetic polymorphisms with trait variation and have already identified causative variants for various traits in many different organisms, from humans to plants. Importantly, many adaptive traits, like the regulation of flowering time in plants, are not regulated by distinct genetic effects, but by more sophisticated gene regulatory networks.
FGF/FGFR signaling regulates embryogenesis, angiogenesis, tissue homeostasis and wound repair by modulating proliferation, differentiation, survival, migration and metabolism of target cells. Understandably, compelling evidence for deregulated FGF signaling in the development and progression of different types of tumors continue to emerge and FGFR inhibitors arise as potential targeted therapeutic agents, particularly in tumors harboring aberrant FGFR signaling. There is first evidence of a dual role of the FGF/FGFR system in both organogenesis and tumorigenesis, of which this review aims to provide an overview. FGF-1 and FGF-2 are expressed in the adrenal cortex and are the most powerful mitogens for adrenocortical cells. Physiologically, they are involved in development and maintenance of the adrenal gland and bind to a family of four tyrosine kinase receptors, among which FGFR1 and FGFR4 are the most strongly expressed in the adrenal cortex. The repeatedly proven overexpression of these two FGFRs also in adrenocortical cancer is thus likely a sign of their participation in proliferation and vascularization, though the exact downstream mechanisms are not yet elucidated. Thus, FGFRs potentially offer novel therapeutic targets also for adrenocortical carcinoma, a type of cancer resistant to conventional antimitotic agents.
Composite optimization problems, where the sum of a smooth and a merely lower semicontinuous function has to be minimized, are often tackled numerically by means of proximal gradient methods as soon as the lower semicontinuous part of the objective function is of simple enough structure. The available convergence theory associated with these methods (mostly) requires the derivative of the smooth part of the objective function to be (globally) Lipschitz continuous, and this might be a restrictive assumption in some practically relevant scenarios. In this paper, we readdress this classical topic and provide convergence results for the classical (monotone) proximal gradient method and one of its nonmonotone extensions which are applicable in the absence of (strong) Lipschitz assumptions. This is possible since, for the price of forgoing convergence rates, we omit the use of descent-type lemmas in our analysis.
Immunization of preterm infants: current evidence and future strategies to individualized approaches
(2022)
Preterm infants are at particularly high risk for infectious diseases. As this vulnerability extends beyond the neonatal period into childhood and adolescence, preterm infants benefit greatly from infection-preventive measures such as immunizations. However, there is an ongoing discussion about vaccine safety and efficacy due to preterm infants’ distinct immunological features. A significant proportion of infants remains un- or under-immunized when discharged from primary hospital stay. Educating health care professionals and parents, promoting maternal immunization and evaluating the potential of new vaccination tools are important means to reduce the overall burden from infectious diseases in preterm infants. In this narrative review, we summarize the current knowledge about vaccinations in premature infants. We discuss the specificities of early life immunity and memory function, including the role of polyreactive B cells, restricted B cell receptor diversity and heterologous immunity mediated by a cross-reactive T cell repertoire. Recently, mechanistic studies indicated that tissue-resident memory (Trm) cell populations including T cells, B cells and macrophages are already established in the fetus. Their role in human early life immunity, however, is not yet understood. Tissue-resident memory T cells, for example, are diminished in airway tissues in neonates as compared to older children or adults. Hence, the ability to make specific recall responses after secondary infectious stimulus is hampered, a phenomenon that is transcriptionally regulated by enhanced expression of T-bet. Furthermore, the microbiome establishment is a dominant factor to shape resident immunity at mucosal surfaces, but it is often disturbed in the context of preterm birth. The proposed function of Trm T cells to remember benign interactions with the microbiome might therefore be reduced which would contribute to an increased risk for sustained inflammation. An improved understanding of Trm interactions may determine novel targets of vaccination, e.g., modulation of T-bet responses and facilitate more individualized approaches to protect preterm babies in the future.
Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it imposes an upper bound to potential correlations with other variables (e.g., symptom or brain data). However, the reliability of many task readouts is low. In this study, we scrutinized the retest reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from N = 40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data were partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We found good to excellent reliability for behavioral indices as derived from mixed-effects models that included data from both sessions. The internal consistency was good to excellent. For indices derived from computational modeling, we found excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences in cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modeling of the longitudinal data (whether sessions are modeled separately or jointly), on estimation methods, and on the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools.
Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.