Inductive Algebras for Finite Heisenberg Groups

A characterization of the maximal abelian sub-algebras of matrix algebras that are normalized by the canonical representation of a finite Heisenberg group is given. Examples are constructed using a classification result for finite Heisenberg groups.Comment: 5 page

Inductive algebras and homogeneous shifts

Inductive algebras for the irreducible unitary representations of the universal cover of the group of unimodular two by two matrices are classified. The classification of homogeneous shift operators is obtained as a direct consequence. This gives a new approach to the results of Bagchi and Misra

Simultaneous sub-second hyperpolarization of the nuclear and electron spins of phosphorus in silicon

We demonstrate a method which can hyperpolarize both the electron and nuclear spins of 31P donors in Si at low field, where both would be essentially unpolarized in equilibrium. It is based on the selective ionization of donors in a specific hyperfine state by optically pumping donor bound exciton hyperfine transitions, which can be spectrally resolved in 28Si. Electron and nuclear polarizations of 90% and 76%, respectively, are obtained in less than a second, providing an initialization mechanism for qubits based on these spins, and enabling further ESR and NMR studies on dilute 31P in 28Si.Comment: 4 pages, 3 figure

A fourth account of centipede (Chilopoda) predation on bats

With an incident in Palo Duro Canyon, Texas, USA, Scolopendra heros Girard (Chilopoda: Scolopendromorpha: Scolopendridae) becomes the third centipede species known to prey on bats; S. gigantea Linnaeus and S. viridicornis Newport have been so documented in Venezuela and Brazil, respectively. The Texas predation was interrupted by the predator/prey pair’s falling around 15–20 m from the canyon wall and, perhaps also, by human presence where they landed. The centipede uncoiled and retreated to shelter under a nearby rock and, after initial immobilization, so did the bat. Con un incidente en Palo Duro Canyon, Texas, Estados Unidos, Scolopendra heros Girard (Chilopoda: Scolopendromorpha: Scolopendridae) se convierte en el tercer ciempiés que se sabe depreda murciélagos; S. gigantea Linnaeus y S. viridicornis Newport han sido documentados mostrando este comportamiento en Venezuela y Brasil, respectivamente. El incidente de Texas fue interrumpido por el despeñamiento del par depredador/presa unos 15–20 m desde la pared del cañón y, al parecer, por la presencia humana en el sitio de caída. El ciempiés se desenrolló y retiró para refugiarse bajo una roca cercana. Tras permanecer inmóvil inicialmente, el murciélago hizo lo mismo

FoxK1 and FoxK2 in insulin regulation of cellular and mitochondrial metabolism

A major target of insulin signaling is the FoxO family of Forkhead transcription factors, which translocate from the nucleus to the cytoplasm following insulin-stimulated phosphorylation. Here we show that the Forkhead transcription factors FoxK1 and FoxK2 are also downstream targets of insulin action, but that following insulin stimulation, they translocate from the cytoplasm to nucleus, reciprocal to the translocation of FoxO1. FoxK1/FoxK2 translocation to the nucleus is dependent on the Akt-mTOR pathway, while its localization to the cytoplasm in the basal state is dependent on GSK3. Knockdown of FoxK1 and FoxK2 in liver cells results in upregulation of genes related to apoptosis and down-regulation of genes involved in cell cycle and lipid metabolism. This is associated with decreased cell proliferation and altered mitochondrial fatty acid metabolism. Thus, FoxK1/K2 are reciprocally regulated to FoxO1 following insulin stimulation and play a critical role in the control of apoptosis, metabolism and mitochondrial function

Exploring functional regression for dynamic modeling of shallow landslides in South Tyrol, Italy

Shallow landslides are ubiquitous hazards in mountainous regions worldwide that arise from an interplay of static predisposing factors and dynamic preparatory and triggering conditions. Modeling shallow landslides at regional scales has leveraged data-driven approaches to separately investigate purely spatial landslide susceptibility and temporally varying conditions. Yet, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. Furthermore, dynamic factors have been typically included in data-driven landslide models as scalar predictors by employing aggregated descriptors over time (e.g., mean, maximum, or total precipitation over a defined time window), where many choices are possible for the considered time scales and aggregation operators. Therefore, incorporating the time-varying behavior of dynamic factors remains difficult.This study addresses these challenges by exploring Functional Generalized Additive Models (FGAMs) to predict the occurrence of shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In contrast to conventional techniques, we test the benefits of using functional predictors to describe dynamic factors (e.g., precipitation and temperature) leading to landslide events. In other words, we evaluate dynamic factors as collections of measurements over time (i.e., time series). To do so, our approach uses a binomial FGAM to analyze the statistical associations between the static factors (scalar predictors), the dynamic weather conditions prior to a potential landslide occurrence (functional predictors), and the occurrence of shallow landslides in space and time.Potential outcomes of this novel approach show an overview of the added value of using functional predictors for space and time shallow landslide modeling. These research findings are positioned within the context of the PROSLIDE project, which has received financial support from the Research Südtirol/Alto Adige 2019 research program of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige

Developing a spatiotemporal model to integrate landslide susceptibility and critical rainfall conditions. A practical model applied to Rio de Janeiro municipality

Despite being a landscape evolution element, landslides pose a significant threat to infrastructure, property, and human life around the globe. In Brazil, this has been a major source of concern for many years. Over the last decades, especially in the humid areas of Brazil, landslide occurrences have become more frequent and catastrophic (Pelech et al., 2019). Especially in large and medium-sized cities, poorly-regulated living conditions and a progressing global warming scenario will likely increase the frequency, magnitude, and possibly damagecaused by landslides (Marengo et al., 2021). On the other hand, despite the efforts of local authorities to forecast and mitigate the phenomena, not enough is currently being done in terms of preparedness for future events, especially concerning research (Dias et al., 2021).Due to the geomorphological and climatic settings, the municipality of Rio de Janeiro (~1,200 km²) is often affected by landslides (Coelho Netto et al., 2007; 2009). According to the Brazilian Institute of Geography and Statistics (IBGE, 2021), the municipality has 6.7 million inhabitants, of which circa 20-25% lives in the favelas. These communities, usually located on hill slopes, face diverse challenges such as poor basic infrastructure, lack of sanitation systems, and high criminality, which tend to diminish the inhabitants’ awareness of potential landslide hazards. On the other hand, the municipality of Rio de Janeiro has systematically tracked rainfall data for the last decades. Such data comprises 33 stations, recording measurements every 15 minutes. Rainfall data is availablefor a few decades and comprise 33 stations recording measurements every 15 minutes. Also, the availability of high-resolution DTM and DEM (obtained through LiDAR with a 15 cm resolution), orthoimagery updated quasiyearly, and a suitable landslide inventory, turns Rio de Janeiro into a promising real-life laboratory for suggesting and enhancing modeling solutions that may provide valuable tools for landslide emergency preparedness, management, and response.Building upon the findings of Steger et al, 2022, the present research represents a joint effort to suggest a methodological framework to develop a dynamic landslide model that integrates static predisposing factors with dynamic rainfall conditions. Data-driven methods (e.g., Generalized Additive Models) will be used to establish statistical relationships between the static factors, the dynamic rainfall conditions prior to a potential landslide, and the landslide occurrence in space and time. The outcomes may be used by stakeholders to strategically prepare for potential rainfall events leading to landslides and possibly to improve early warning systems. Data collection and preparation are currently happening, and the analysis will follow. Partial results will be presented at the 6th World Landslide Forum

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project