Autoencoder Attractors for Uncertainty Estimation

The reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: The recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. The use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.Comment: This paper is accepted at IEEE International Conference on Pattern Recognition (ICPR), 202

Enhancing motivation and learning in engineering courses: a challenge-based approach to teaching embedded systems

This paper addresses an approach to teaching embedded systems programming through a challenge-based competition involving robots. This pedagogical project distinguishes itself by incorporating international students from three international institutions through the Blended Intensive Program (BIP). The research findings indicate that this approach yields excellent results regarding student engagement and learning outcomes. The challenge-based program effectively promotes students’ creative problem-solving abilities by combining theoretical instruction with hands-on experience in a competitive setting.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021) and project LA/P/0063/2020. This work was supported by Blended Intensive Programme ID: 2021- 1-PT01-KA131-HED-000004268-2, Embedded Systems Applications. The authors thank CEFET/RJ, the Institute of Engineering and the Research Centre on Bio-based Economy of Hanze University of Applied Sciences, the ERASMUS program, and the Brazilian research agencies CAPES, CNPq, and FAPERJ.info:eu-repo/semantics/publishedVersio

Approaches to consider heterogeneity - task adaptation for inclusive mathematics education in a professional development program

Der Adaption von Aufgaben kommt im inklusiven Unterricht eine besondere Bedeutung zu. In diesem Beitrag werden Maßnahmen zur Berücksichtigung von Heterogenität vorgestellt, die Lehrkräfte bei der Anforderung, Aufgaben für den Einsatz im inklusiven Mathematikunterricht zu adaptieren, nutzen können. Sowohl die theoretische und empirische Fundierung der Maßnahmen als auch ihr vielseitiger Einsatz in einer digitalen Fortbildung für Lehrkräfte der Sekundarstufe I werden erläutert. Die beispielhaften Konkretisierungen, wie die Maßnahmen in der Fortbildung verwendet wurden, zeigen auf, wie die Adaption von Aufgaben für inklusiven Mathematikunterricht in einer Fortbildung thematisiert werden kann. Es wird dargestellt, wie die Maßnahmen in der Fortbildung im Kontext einer Geometrieaufgabe erläutert wurden, wie diese auf eine Lernumgebung zur Einführung in Wahrscheinlichkeit angewendet wurden und inwiefern die Lehrkräfte die Maßnahmen für die weitere Adaption von Aufgaben auch vor dem Hintergrund ihres eigenen Unterrichts nutzten. Dabei wird durchweg eine Verbindung von Fach- und Entwicklungsorientierung herausgearbeitet, indem fachlich-fachdidaktische Überlegungen und entwicklungsorientierte Ausführungen insbesondere in den Bereichen Lernentwicklung sowie Entwicklung des sprachlichen und kommunikativen Handelns verknüpft werden. (DIPF/Orig.)The adaptation of tasks is particularly important in inclusive settings. This article presents approaches to consider heterogeneity, which teachers can use to adapt tasks for inclusive mathematics settings. The theoretical and empirical foundation of the approaches as well as their versatile use in a digital professional development (PD) program for secondary teachers are explained. The exemplarily concretion of how the approaches were used in the PD program focus on how the adaptation of tasks for inclusive mathematics teaching can be addressed in a PD program. It is shown how the approaches were explained in the PD program in the context of a geometry task, how these were applied to a learning environment on introducing probability and to what extent the teachers used the approaches for the further adaptation of tasks also against the background of their own teaching practice. Throughout, a connection between a subject-specific view and a developmental orientation is considered by linking subject-specific considerations and development-oriented explanations especially concerning students with special needs regarding learning difficulties and low (academic) language proficiency. (DIPF/Orig.

Intermediate Phenotypes Identify Divergent Pathways to Alzheimer's Disease

Background: Recent genetic studies have identified a growing number of loci with suggestive evidence of association with susceptibility to Alzheimer's disease (AD). However, little is known of the role of these candidate genes in influencing intermediate phenotypes associated with a diagnosis of AD, including cognitive decline or AD neuropathologic burden. Methods/Principal Findings: Thirty-two single nucleotide polymorphisms (SNPs) previously implicated in AD susceptibility were genotyped in 414 subjects with both annual clinical evaluation and completed brain autopsies from the Religious Orders Study and the Rush Memory and Aging Project. Regression analyses evaluated the relation of SNP genotypes to continuous measures of AD neuropathology and cognitive function proximate to death. A SNP in the zinc finger protein 224 gene (ZNF224, rs3746319) was associated with both global AD neuropathology (p = 0.009) and global cognition (p = 0.002); whereas, a SNP at the phosphoenolpyruvate carboxykinase locus (PCK1, rs8192708) was selectively associated with global cognition (p = 3.57×10−4). The association of ZNF224 with cognitive impairment was mediated by neurofibrillary tangles, whereas PCK1 largely influenced cognition independent of AD pathology, as well as Lewy bodies and infarcts. Conclusions/Significance: The findings support the association of several loci with AD, and suggest how intermediate phenotypes can enhance analysis of susceptibility loci in this complex genetic disorder

CD10, BCL6, and MUM1 expression in diffuse large B-cell lymphoma on FNA samples

Gene expression profiling has divided diffuse large B-cell lymphoma (DLBCL) into 2 main subgroups: germinal center B (GCB) and non-GCB type. This classification is reproducible by immunohistochemistry using specific antibodies such as CD10, B-cell lymphoma 6 (BCL6), and multiple myeloma oncogene 1 (MUM1). Fine-needle aspiration (FNA) plays an important role in the diagnosis of non-Hodgkin lymphoma, and in some cases FNA may be the only available pathological specimen. The objectives of the current study were to evaluate CD10, BCL6, and MUM1 immunostaining on FNA samples by testing the CD10, BCL6, and MUM1 algorithm on both FNA cell blocks (CB) and conventional smears (CS), evaluating differences in CB and CS immunocytochemical (ICC) performance, and comparing results with histological data

Multi-Way Multi-Group Segregation and Diversity Indices

Background: How can we compute a segregation or diversity index from a three-way or multi-way contingency table, where each variable can take on an arbitrary finite number of values and where the index takes values between zero and one? Previous methods only exist for two-way contingency tables or dichotomous variables. A prototypical three-way case is the segregation index of a set of industries or departments given multiple explanatory variables of both sex and race. This can be further extended to other variables, such as disability, number of years of education, and former military service. Methodology/Principal Findings: We extend existing segregation indices based on Euclidean distance (square of coefficient of variation) and Boltzmann/Shannon/Theil index from two-way to multi-way contingency tables by including multiple summations. We provide several biological applications, such as indices for age polyethism and linkage disequilibrium. We also provide a new heuristic conceptualization of entropy-based indices. Higher order association measures are often independent of lower order ones, hence an overall segregation or diversity index should be the arithmetic mean of the normalized association measures at all orders. These methods are applicable when individuals selfidentify as multiple races or even multiple sexes and when individuals work part-time in multiple industries. Conclusions/Significance: The policy implications of this work are enormous, allowing people to rigorously test whethe

Phosphofructo-2-kinase/Fructose-2,6-bisphosphatase Modulates Oscillations of Pancreatic Islet Metabolism

Pulses of insulin from pancreatic beta-cells help maintain blood glucose in a narrow range, although the source of these pulses is unclear. It has been proposed that a positive feedback circuit exists within the glycolytic pathway, the autocatalytic activation of phosphofructokinase-1 (PFK1), which endows pancreatic beta-cells with the ability to generate oscillations in metabolism. Flux through PFK1 is controlled by the bifunctional enzyme PFK2/FBPase2 (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase) in two ways: via (1) production/degradation of fructose-2,6-bisphosphate (Fru2,6-BP), a potent allosteric activator of PFK1, as well as (2) direct activation of glucokinase due to a protein-protein interaction. In this study, we used a combination of live-cell imaging and mathematical modeling to examine the effects of inducibly-expressed PFK2/FBPase2 mutants on glucose-induced Ca2+ pulsatility in mouse islets. Irrespective of the ability to bind glucokinase, mutants of PFK2/FBPase2 that increased the kinase:phosphatase ratio reduced the period and amplitude of Ca2+ oscillations. Mutants which reduced the kinase:phosphatase ratio had the opposite effect. These results indicate that the main effect of the bifunctional enzyme on islet pulsatility is due to Fru2,6-BP alteration of the threshold for autocatalytic activation of PFK1 by Fru1,6-BP. Using computational models based on PFK1-generated islet oscillations, we then illustrated how moderate elevation of Fru-2,6-BP can increase the frequency of glycolytic oscillations while reducing their amplitude, with sufficiently high activation resulting in termination of slow oscillations. The concordance we observed between PFK2/FBPase2-induced modulation of islet oscillations and the models of PFK1-driven oscillations furthermore suggests that metabolic oscillations, like those found in yeast and skeletal muscle, are shaped early in glycolysis

Lactate Protects Microglia and Neurons from Oxygen-Glucose Deprivation/Reoxygenation

Lactate has received attention as a potential therapeutic intervention for brain diseases, particularly those including energy deficit, exacerbated inflammation, and disrupted redox status, such as cerebral ischemia. However, lactate roles in metabolic or signaling pathways in neural cells remain elusive in the hypoxic and ischemic contexts. Here, we tested the effects of lactate on the survival of a microglial (BV-2) and a neuronal (SH-SY5Y) cell lines during oxygen and glucose deprivation (OGD) or OGD followed by reoxygenation (OGD/R). Lactate signaling was studied by using 3,5-DHBA, an exogenous agonist of lactate receptor GPR81. Inhibition of lactate dehydrogenase (LDH) or monocarboxylate transporters (MCT), using oxamate or 4-CIN, respectively, was performed to evaluate the impact of lactate metabolization and transport on cell viability. The OGD lasted 6 h and the reoxygenation lasted 24 h following OGD (OGD/R). Cell viability, extracellular lactate concentrations, microglial intracellular pH and TNF-ɑ release, and neurite elongation were evaluated. Lactate or 3,5-DHBA treatment during OGD increased microglial survival during reoxygenation. Inhibition of lactate metabolism and transport impaired microglial and neuronal viability. OGD led to intracellular acidification in BV-2 cells, and reoxygenation increased the release of TNF-ɑ, which was reverted by lactate and 3,5-DHBA treatment. Our results suggest that lactate plays a dual role in OGD, acting as a metabolic and a signaling molecule in BV-2 and SH-SY5Y cells. Lactate metabolism and transport are vital for cell survival during OGD. Moreover, lactate treatment and GPR81 activation during OGD promote long-term adaptations that potentially protect cells against secondary cell death during reoxygenation