A
bstract
Many kink solutions enjoy internal excitations, called shape modes. In some 1+1d scalar models, such as the
ϕ
4
double-well model, when a kink’s shape mode is excited twice it may decay to ...a ground state kink plus a meson. We analytically calculate the decay rates of both the twice-excited shape mode and also a coherent state corresponding to the classically excited shape mode. In the case of the
ϕ
4
model, we find that the latter agrees with the classical result of Manton and Merabet.
We present the idea of using the holonomy along a line of a constrained instanton solution (an approximate solution of the Euclidean equations of motion subject to a constraint) of an SU(2) ...Yang-Mills-Higgs theory to approximate the Skyrmion solution in a chiral model with massive pions. The fact that the gauge field acquires a nonzero mass due to the Higgs mechanism implies that a constrained instanton decays exponentially far from its center, and so does the Skyrmion configuration that it generates via the Atiyah-Manton construction. This is precisely the desired behavior at large distances for the true Skyrmion solutions when the pion mass is included.
Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to ...the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.
Atomically-sharp tips in close proximity of metal surfaces create plasmonic nanocavities supporting both radiative (bright) and non-radiative (dark) localized surface plasmon modes. Disentangling ...their respective contributions to the total density of optical states remains a challenge. Electroluminescence due to tunnelling through the tip-substrate gap could allow the identification of the radiative component, but this information is inherently convoluted with that of the electronic structure of the system. In this work, we present a fully experimental procedure to eliminate the electronic-structure factors from the scanning tunnelling microscope luminescence spectra by confronting them with spectroscopic information extracted from elastic current measurements. Comparison against electromagnetic calculations demonstrates that this procedure allows the characterization of the meV shifts experienced by the nanocavity plasmonic modes under atomic-scale gap size changes. Therefore, the method gives access to the frequency-dependent radiative Purcell enhancement that a microscopic light emitter would undergo when placed at such nanocavity.
We propose a new equation of state for nuclear matter based on a generalized Skyrme model which is consistent with all current constraints on the observed properties of neutron stars. This ...generalized model depends only on two free parameters related to the ranges of pressure values at which different submodels are dominant, and which can be adjusted so that mass-radius and deformability constraints from astrophysical and gravitational wave measurements can be met. Our results support the Skyrme model and its generalizations as good candidates for a low energy effective field-theoretic description of nuclear matter even at extreme conditions such as those inside neutron stars.
Application of polyester-degrading enzymes should be considered as an eco-friendly alternative to chemical recycling due to the huge plastic waste disposal nowadays. Many hydrolases from several ...fungi and bacteria have been discovered and successfully evaluated for their activity towards different aliphatic polyesters (PHA, PBS, PBSA, PCL, PLA), aromatic polyesters (PET, PBT, PMT) as well as their co-polyesters (PBST, PBAT, PBSTIL). This revision gives an up-to-date overview on the main biochemical features and biotechnological applications of those reported enzymes which are able to degrade polyester-based plastics, including different microbial polyester depolymerases, esterases, cutinase-like enzymes and lipases. Summarized information includes available protein sequences with the corresponding accession numbers deposited in NCBI server, 3D resolved structures, and data about optimal conditions for enzymatic activity and stability of many of these microbial enzymes that would be helpful for researchers in this topic. Although screening and identification of new native polyester hydrolases from microbial sources is undeniable according to literature, we briefly highlight the importance of the design of improved enzymes towards recalcitrant aromatic polyesters through different approaches that include site-directed mutagenesis and surface protein engineering.
•Application of polyester-degrading enzymes as an eco-friendly alternative to chemical recycling of plastics is assessed.•Main biochemical features of enzymes involved in the degradation of aliphatic and aromatic polyesters are reviewed.•Key information of polyester-degrading depolymerases, esterases, cutinase-like enzymes and lipases is summarized.
The impact of LMO2 expression on cell lineage decisions during T‐cell leukemogenesis remains largely elusive. Using genetic lineage tracing, we have explored the potential of LMO2 in dictating a ...T‐cell malignant phenotype. We first initiated LMO2 expression in hematopoietic stem/progenitor cells and maintained its expression in all hematopoietic cells. These mice develop exclusively aggressive human‐like T‐ALL. In order to uncover a potential exclusive reprogramming effect of LMO2 in murine hematopoietic stem/progenitor cells, we next showed that transient LMO2 expression is sufficient for oncogenic function and induction of T‐ALL. The resulting T‐ALLs lacked LMO2 and its target‐gene expression, and histologically, transcriptionally, and genetically similar to human LMO2‐driven T‐ALL. We next found that during T‐ALL development, secondary genomic alterations take place within the thymus. However, the permissiveness for development of T‐ALL seems to be associated with wider windows of differentiation than previously appreciated. Restricted Cre‐mediated activation of Lmo2 at different stages of B‐cell development induces systematically and unexpectedly T‐ALL that closely resembled those of their natural counterparts. Together, these results provide a novel paradigm for the generation of tumor T cells through reprogramming in vivo and could be relevant to improve the response of T‐ALL to current therapies.
Synopsis
Genetic lineage tracing in cell type‐specific mouse models of T‐cell lymphoblastic leukemia (T‐ALL) reveals that tumor cell identity is imposed by expression of the oncogene LMO2, rather than by the target cell phenotype.
Maintained conditional expression of LMO2 in the hematopoietic lineage results in aggressive T‐ALLin vivo.
Restricted early LMO2 expression in hematopoietic stem/progenitor cells is sufficient to induce histological, genomic and transcriptional features of human T‐ALL.
Thymus deficiency impedes secondary genomic alterations required for T‐ALL development.
B‐cell‐specific LMO2 expression reprograms pro‐B cells and germinal center B cells into T‐ALL cells.
The tumor cell state in mouse T‐cell leukemias is dictated by LMO2 oncogene expression independently of the target cell phenotype.
Ageing entails changes in complex cognitive functions that lead to a decrease in autonomy and quality of life. Everyday cognition is the ability to solve cognitively complex problems in the everyday ...world, enabling instrumental activities of life. Benefits have been found in studies using everyday cognition-based assessment and intervention, as the results predict improvements in everyday performance, not just in specific cognitive functions. A study protocol is presented based on assessment and training in everyday cognition versus traditional cognitive stimulation for the improvement of functionality, emotional state, frailty and cognitive function.
A parallel randomised controlled clinical trial with two arms will be conducted. It will be carried out by the University of Salamanca (Spain) in eleven centres and associations for the elderly of the City Council of Salamanca. People aged 60 years or older without cognitive impairment will be recruited. Participants will be randomly distributed into two groups: the experimental group will undergo a training programme in everyday cognition and the control group a programme of traditional cognitive stimulation, completing 25 sessions over 7 months. All participants will be assessed at the beginning and at the end of the intervention, where socio-demographic data and the following scales will be collected: The Medical Outcomes Study (MOS), Questionnaire ARMS-e, Everyday Cognition Test (PECC), Scale Yesavage, Test Montreal Cognitive Assessment (MoCA), The Functional Independence Measure (FIM), Fragility Index and Lawton y Brody Scale.
The present study aims to improve conventional clinical practice on cognitive function training by proposing a specific assessment and intervention of everyday cognition based on the importance of actual cognitive functioning during the resolution of complex tasks of daily life, giving priority to the improvement of autonomy.
ClinicalTrials.gov; ID: NCT05688163. Registered on: January 18, 2023.
Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, ...more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed.
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•Approach to fire severity mapping from unitemporal Landsat-8 and Sentinel-2 data•Accurate estimation of fire severity from synthetic spectral data and Random Forest•Successful application to a Mediterranean burned area, with high adaptability•Towards improvement of fire severity mapping for forest management practices
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