Minimal quantum dot based Kitaev chain with only local superconducting proximity effect

The possibility to engineer a Kitaev chain in quantum dots coupled via superconductors has recently emerged as a promising path toward topological superconductivity and possibly nonabelian physics. Here, we show that it is possible to avoid some of the main experimental hurdles on this path by using only local proximity effect on each quantum dot in a geometry that resembles a two-dot version of the proposal in New J. Phys. 15 045020 (2013). There is no need for narrow superconducting couplers, additional Andreev bound states, or spatially varying magnetic fields; it suffices with spin-orbit interaction and a constant magnetic field, in combination with control of the superconducting phase to tune the relative strengths of elastic cotunneling and an effective crossed-Andreev-reflection-like process generated by higher-order tunneling. We use a realistic spinful, interacting model and show that high-quality Majorana bound states can be generated already in a double quantum dot.Comment: 11 pages, 5 figures, v2: slightly revised argument in section III (results unchanged), minor correction to Fig. 2, minor improvements in the text, title changed (matches published version

Distinct Lysosomal Network Protein Profiles in Parkinsonian Syndrome Cerebrospinal Fluid.

BackgroundClinical diagnosis of parkinsonian syndromes like Parkinson's disease (PD), corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP) is hampered by overlapping symptomatology and lack of diagnostic biomarkers, and definitive diagnosis is only possible post-mortem.ObjectiveSince impaired protein degradation plays an important role in many neurodegenerative disorders, we hypothesized that profiles of select lysosomal network proteins in cerebrospinal fluid could be differentially expressed in these parkinsonian syndromes.MethodsCerebrospinal fluid samples were collected from PD patients (n = 18), clinically diagnosed 4-repeat tauopathy patients; corticobasal syndrome (CBS) (n = 3) and PSP (n = 8); and pathologically diagnosed PSP (n = 8) and CBD patients (n = 7). Each patient set was compared to its appropriate control group consisting of age and gender matched individuals. Select lysosomal network protein levels were detected via Western blotting. Factor analysis was used to test the diagnostic sensitivity, specificity and accuracy of the select lysosomal network protein expression profiles.ResultsPD, CBD and PSP were markedly different in their cerebrospinal fluid lysosomal network protein profiles. Lysosomal-associated membrane proteins 1 and 2 were significantly decreased in PD; early endosomal antigen 1 was decreased and lysozyme increased in PSP; and lysosomal-associated membrane proteins 1 and 2, microtubule-associated protein 1 light chain 3 and lysozyme were increased in CBD. A panel of lysosomal-associated membrane protein 2, lysozyme and microtubule-associated protein 1 light chain discriminated between controls, PD and 4-repeat tauopathies.ConclusionsThis study offers proof of concept that select lysosomal network proteins are differentially expressed in cerebrospinal fluid of Parkinson's disease, corticobasal syndrome and progressive supranuclear palsy. Lysosomal network protein analysis could be further developed as a diagnostic fluid biomarker in parkinsonian syndromes

Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, there are conjugate priors that enable us to express the multi-object posterior in closed form, which could theoretically provide Bayes-optimal estimates. However, the posterior involves a super-exponential growth of the number of hypotheses over time, forcing state-of-the-art methods to resort to approximations for remaining tractable, which can impact their performance in complex scenarios. Model-free methods based on deep-learning provide an attractive alternative, as they can, in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available. In this paper, we propose a high-performing deep-learning method for MTT based on the Transformer architecture and compare it to two state-of-the-art Bayesian filters, in a setting where we assume the correct model is provided. Although this gives an edge to the model-based filters, it also allows us to generate unlimited training data. We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime. The code for all our implementations is made available at https://github.com/JulianoLagana/MT3 .Comment: 8 pages, 4 figure

Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, there are conjugate priors that enable us to express the multi-object posterior in closed form, which could theoretically provide Bayes-optimal estimates. However, the posterior involves a super-exponential growth of the number of hypotheses over time, forcing state-of-the-art methods to resort to approximations for remaining tractable, which can impact their performance in complex scenarios. Model-free methods based on deep-learning provide an attractive alternative, as they can, in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available. In this paper, we propose a high-performing deeplearning method for MTT based on the Transformer architecture and compare it to two state-of-the-art Bayesian filters, in a setting where we assume the correct model is provided. Although this gives an edge to the model-based filters, it also allows us to generate unlimited training data. We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime. The code for all our implementations is made available at https://github.com/JulianoLagana/MT3

Results of matching valve and root repair to aortic valve and root pathology

ObjectiveFor patients with aortic root pathology and aortic valve regurgitation, aortic valve replacement is problematic because no durable bioprosthesis exists, and mechanical valves require lifetime anticoagulation. This study sought to assess outcomes of combined aortic valve and root repair, including comparison with matched bioprosthesis aortic valve replacement.MethodsFrom November 1990 to January 2005, 366 patients underwent modified David reimplantation (n = 72), root remodeling (n = 72), or valve repair with sinotubular junction tailoring (n = 222). Active follow-up was 99% complete, with a mean of 5.6 ± 4.0 years (maximum 17 years); follow-up for vital status averaged 8.5 ± 3.6 years (maximum 19 years). Propensity-adjusted models were developed for fair comparison of outcomes.ResultsThirty-day and 5-, 10-, and 15-year survivals were 98%, 86%, 74%, and 58%, respectively, similar to that of the US matched population and better than that after bioprosthesis aortic valve replacement. Propensity-score–adjusted survival was similar across procedures (P > .3). Freedom from reoperation at 30 days and 5 and 10 years was 99%, 92%, and 89%, respectively, and was similar across procedures (P > .3) after propensity-score adjustment. Patients with tricuspid aortic valves were more likely to be free of reoperation than those with bicuspid valves at 10 years (93% vs 77%, P = .002), equivalent to bioprosthesis aortic valve replacement and superior after 12 years. Bioprostheses increasingly deteriorated after 7 years, and hazard functions for reoperation crossed at 7 years.ConclusionsValve preservation (rather than replacement) and matching root procedures have excellent early and long-term results, with increasing survival benefit at 7 years and fewer reoperations by 12 years. We recommend this procedure for experienced surgical teams

Economic viability of protein concentrate production from green biomass of intermediate crops: A pre-feasibility study

Green biomass is a major potential source of proteins for food and feed. This pre-feasibility study evaluates the use of green biomass of buckwheat, phacelia, hemp and oilseed radish grown as intermediate crops (IC) as a feedstock for production of protein concentrates to produce protein-rich food and feed products. We investigated the biomass yield, protein concentration and protein recovery potential of non-fertilized IC, nitrogen-fertilized IC and IC intercropped with legumes, harvested in late summer to autumn during 2017 and 2018 in southern Sweden. In addition, economic assessment of potential protein and fibre feed and food products were evaluated. The results showed that IC fertilized with 40 kg ha1 N and intercropping with legumes contributed to a higher biomass dry matter (DM) yield of 4.9e5.8 t ha1 as compared to between 2.2 and 3.1 t ha1 for non-fertilized IC. Intercropping with legumes also resulted in higher protein yield of 154 g kg1 vs. 103 g kg1 for non-fertilized IC. Among IC, hemp, phacelia and oilseed radish showed up to ca. 25% higher DM yield and up to ca. 70% higher protein concentration as compared to buckwheat. Higher DM yield was obtained when IC were harvested in October and November than in August and September. Economic assessment was made on two feasible protein production pathways; (A) Green and white proteins and (B) total recoverable combined protein fraction (CPF). For all IC, cost per t DM was higher in August due to lower biomass yield as compared to other harvesting months. Nitrogen concentration was the main factor determining the size of revenues. Nitrogen concentration was 34% higher in 2018 compared to 2017 and therefore resulted in higher revenues in that year. Intercropping resulted in higher protein content and therefore contributed to lower breakeven prices of recovered green proteins for all IC. Breakeven price analyses showed that green protein and CPF were economically feasible to market as both bulk and premium products depending on lower (2 V kg1 ) or higher (2e10 V kg1 ) price ranges, respectively. The results demonstrate that use of IC biomass could be a feasible option to produce high value protein-rich products, which can contribute extra income from IC for farmers

Selective Generation of Gut Tropic T Cells in Gut-associated Lymphoid Tissue (GALT): Requirement for GALT Dendritic Cells and Adjuvant

In the current study, we address the underlying mechanism for the selective generation of gut-homing T cells in the gut-associated lymphoid tissues (GALT). We demonstrate that DCs in the GALT are unique in their capacity to establish T cell gut tropism but in vivo only confer this property to T cells in the presence of DC maturational stimuli, including toll-like receptor-dependent and -independent adjuvants. Thus, DCs from mesenteric LNs (MLNs), but not from spleen, supported expression of the chemokine receptor CCR9 and integrin α4β7 by activated CD8+ T cells. While DCs were also required for an efficient down-regulation of CD62L, this function was not restricted to MLN DCs. In an adoptive CD8+ T cell transfer model, antigen-specific T cells entering the small intestinal epithelium were homogeneously CCR9+α4β7+CD62Llow, and this phenotype was only generated in GALT and in the presence of adjuvant. Consistent with the CCR9+ phenotype of the gut-homing T cells, CCR9 was found to play a critical role in the localization of T cells to the small intestinal epithelium. Together, these results demonstrate that GALT DCs and T cell expression of CCR9 play critical and integrated roles during T cell homing to the gut

Functional specialization of gut CD103+ dendritic cells in the regulation of tissue-selective T cell homing

Gut-associated lymphoid tissue (GALT) dendritic cells (DCs) display a unique ability to generate CCR9+α4β7+ gut-tropic CD8+ effector T cells. We demonstrate efficient induction of CCR9 and α4β7 on CD8+ T cells in mesenteric lymph nodes (MLNs) after oral but not intraperitoneal (i.p.) antigen administration indicating differential targeting of DCs via the oral route. In vitro, lamina propria (LP)–derived DCs were more potent than MLN or Peyer's patch DCs in their ability to generate CCR9+α4β7+ CD8+ T cells. The integrin α chain CD103 (αE) was expressed on almost all LP DCs, a subset of MLN DCs, but on few splenic DCs. CD103+ MLN DCs were reduced in number in CCR7−/− mice and, although CD8+ T cells proliferated in the MLNs of CCR7−/− mice after i.p. but not oral antigen administration, they failed to express CCR9 and had reduced levels of α4β7. Strikingly, although CD103+ and CD103− MLN DCs were equally potent at inducing CD8+ T cell proliferation and IFN-γ production, only CD103+ DCs were capable of generating gut-tropic CD8+ effector T cells in vitro. Collectively, these results demonstrate a unique function for LP-derived CD103+ MLN DCs in the generation of gut-tropic effector T cells