Gender-specific differences of bronchial asthma phenotypes in children depending on puberty status

Further investigations are needed to examine the effect of gender-specific differences in changes of asthma prevalence and phenotypes in pre- and post puberty. Objective of the study was to evaluate if sex-based differences exist in clinical and epidemiologic characteristics of asthma in children before and after puberty

Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via the digitization of the counterdiabatic acceleration of a given adiabatic quantum computation. Finding the lowest energy configuration of the amino acid sequence is an NP-hard optimization problem that plays a prominent role in chemistry, biology, and drug design. We outperform state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on quantum hardware. Specifically, we benchmark our quantum algorithm with Quantinuum's trapped ions, Google's and IBM's superconducting circuits, obtaining high success probabilities with low-depth circuits as required in the NISQ era

Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete mean-variance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.Comment: 8 pages, 4 figure

Portfolio optimization with digitized counterdiabatic quantum algorithms

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete meanvariance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.This work is supported by NSFC (Grant No. 12075145) , STCSM (Grant No. 2019SHZDZX01-ZX04) , EU FET Open Grant EPIQUS (No. 899368) , QUANTEK project (Grant No. KK-2021/00070) , the Basque Government through Grant No. IT1470-22, the project Grant No. PID2021-126273NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe and ERDF Invest in your Future and the Ramon y Cajal program (Grant No. RYC-2017-22482) . F. A. -A. acknowledges ANID Subvencion a la Instalacion en la Academia SA77210018 ANID Proyecto Basal AFB 180001. Authors would also like to acknowledge the Azure quantum credits program for providing access to the Quantinuum H1 emulator

Understanding emotionally relevant situations in primary dental practice. 2. Reported effects of emotionally charged situations

Background and aims. Dentistry is widely reported to be a stressful profession. There is a limited body of research relating to the coping strategies used by dentists whilst in clinical situations. This study aims to use qualitative methods to explore the full extent of the coping strategies associated with stressful events in primary dental practice. Method. Semi-structured interviews were conducted with 20 dentists within a 50 mile radius of Lincoln. A thematic analysis was conducted on verbatim transcriptions thereby identifying six themes and 35 codes. Results. Participants described both problem-focused and emotion-focused strategies. The strategies used had a variety of outcomes in the context of use. Most dentists denied that their emotions affected their decision-making, but then proceeded to describe how they were influential. Discussion and conclusion. Dentists use a wide variety of coping strategies some of which are maladaptive. Training in the development and recognition of appropriate coping decisions would be appropriate as they woul

Digitized-counterdiabatic quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) has proved to be an e ective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitizedcounterdiabatic QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms standard QAOA in all cases we study

FOXR2 Targets LHX6+/DLX+ Neural Lineages to Drive Central Nervous System Neuroblastoma

Central nervous system neuroblastoma with forkhead box R2 (FOXR2) activation (NB-FOXR2) is a high-grade tumor of the brain hemispheres and a newly identified molecular entity. Tumors express dual neuronal and glial markers, leading to frequent misdiagnoses, and limited information exists on the role of FOXR2 in their genesis. To identify their cellular origins, we profiled the transcriptomes of NB-FOXR2 tumors at the bulk and single-cell levels and integrated these profiles with large single-cell references of the normal brain. NB-FOXR2 tumors mapped to LHX6+/DLX+ lineages derived from the medial ganglionic eminence, a progenitor domain in the ventral telencephalon. In vivo prenatal Foxr2 targeting to the ganglionic eminences in mice induced postnatal cortical tumors recapitulating human NB-FOXR2-specific molecular signatures. Profiling of FOXR2 binding on chromatin in murine models revealed an association with ETS transcriptional networks, as well as direct binding of FOXR2 at key transcription factors that coordinate initiation of gliogenesis. These data indicate that NB-FOXR2 tumors originate from LHX6+/DLX+ interneuron lineages, a lineage of origin distinct from that of other FOXR2-driven brain tumors, highlight the susceptibility of ventral telencephalon-derived interneurons to FOXR2-driven oncogenesis, and suggest that FOXR2-induced activation of glial programs may explain the mixed neuronal and oligodendroglial features in these tumors. More broadly, this work underscores systematic profiling of brain development as an efficient approach to orient oncogenic targeting for in vivo modeling, critical for the study of rare tumors and development of therapeutics. Significance: Profiling the developing brain enabled rationally guided modeling of FOXR2-activated CNS neuroblastoma, providing a strategy to overcome the heterogeneous origins of pediatric brain tumors that hamper tumor modeling and therapy development. See related commentary by Orr, p. 195

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging

Objective This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol. Materials and methods Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter. Results Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain500,1000, kidney300,1000, and liver200,1000. Conclusion IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability