The Self-Energy of Massive Lattice Fermions

We address the perturbative renormalization of massive lattice fermions. We derive expressions-valid to all orders in perturbation theory and for all values of the bare fermion mass-for the rest mass, the kinetic mass, and the wave-function renormalization factor. We obtain the fermion's self energy at the one-loop level with a mass-dependent, $O(a)$ improved action. Numerical results for two interesting special cases, the Wilson and Sheikholeslami-Wohlert actions, are given. The mass dependence of these results smoothly connects the massless and infinite-mass limits, as expected. Combined with Monte Carlo calculations our results can be employed to determine the quark masses in common renormalization schemes.Comment: 33 pages; 11 figures (included

The Role of Expert Opinion in Projecting Long-Term Survival Outcomes Beyond the Horizon of a Clinical Trial

INTRODUCTION: Clinical trials often have short follow-ups, and long-term outcomes such as survival must be extrapolated. Current extrapolation methods often produce a wide range of survival values. To minimize uncertainty in projections, we developed a novel method that incorporates formally elicited expert opinion in a Bayesian analysis and used it to extrapolate survival in the placebo arm of DAPA-CKD, a phase 3 trial of dapagliflozin in patients with chronic kidney disease (NCT03036150). METHODS: A summary of mortality data from 13 studies that included DAPA-CKD-like populations and training on elicitation were provided to six experts. An elicitation survey was used to gather the experts' 10- and 20-year survival estimates for patients in the placebo arm of DAPA-CKD. These estimates were combined with DAPA-CKD mortality and general population mortality (GPM) data in a Bayesian analysis to extrapolate long-term survival using seven parametric distributions. Results were compared with those from standard frequentist approaches (with and without GPM data) that do not incorporate expert opinion. RESULTS: The group expert-elicited estimate for 20-year survival was 31% (lower estimate, 10%; upper estimate, 40%). In the Bayesian analysis, the 20-year extrapolated survival across the seven distributions was 14.9-39.1%, a range that was 2.4- and 1.6-fold smaller than those produced by the frequentist methods (0.0-56.9% without and 0.0-39.2% with GPM data). CONCLUSIONS: Using expert opinion in a Bayesian analysis provided a robust method for extrapolating long-term survival in the placebo arm of DAPA-CKD. The method could be applied to other populations with limited survival data

Uptake and accumulation of emerging contaminants in processing tomato irrigated with tertiary treated wastewater effluent: a pilot-scale study

The reuse of treated wastewater for crop irrigation is vital in water-scarce semi-arid regions. However, concerns arise regarding emerging contaminants (ECs) that persist in treated wastewater and may accumulate in irrigated crops, potentially entering the food chain and the environment. This pilot-scale study conducted in southern Italy focused on tomato plants (Solanum lycopersicum L. cv Taylor F1) irrigated with treated wastewater to investigate EC uptake, accumulation, and translocation processes. The experiment spanned from June to September 2021 and involved three irrigation strategies: conventional water (FW), treated wastewater spiked with 10 target contaminants at the European average dose (TWWx1), and tertiary WWTP effluent spiked with the target contaminants at a triple dose (TWWx3). The results showed distinct behavior and distribution of ECs between the TWWx1 and TWWx3 strategies. In the TWWx3 strategy, clarithromycin, carbamazepine, metoprolol, fluconazole, and climbazole exhibited interactions with the soil-plant system, with varying degradation rates, soil accumulation rates, and plant accumulation rates. In contrast, naproxen, ketoprofen, diclofenac, sulfamethoxazole, and trimethoprim showed degradation. These findings imply that some ECs may be actively taken up by plants, potentially introducing them into the food chain and raising concerns for humans and the environment

Patterns of variation in plant diversity vary over different spatial levels in seasonal coastal wetlands

Aim: To quantify the responses of alpha and beta diversity to multivariate gradients, incorporating variation in environmental and management variability in coastal dune slacks. Location: United Kingdom dune slacks. Methods: Plant community composition, plant nutrient status and soil characteristics were measured for 164 quadrats in 41 dune slacks across 12 coastal sand dune systems. Data were collated on climate and atmospheric deposition. Hydrological regimes at daily resolution were modelled and calibrated using daily-to-monthly site measurements, from which we calculated quadrat-level hydrological metrics. Alpha diversity (richness, Shannon diversity and Pielou's evenness) metrics and beta diversity (turnover and nestedness) for species and genera were calculated across three spatial levels from sand dune system (highest) to dune slack to quadrat (lowest). Results: Diversity patterns depended on the spatial and taxonomic level considered. At smaller spatial levels (between dune slacks and between quadrats), alpha and beta diversity varied along gradients driven by soil characteristics, water table depth and atmospheric deposition. At larger spatial levels (between sand dune systems), patterns of beta diversity were a consequence of plant nutrient status. There was little variability in alpha diversity along this same gradient, with only small changes in Pielou's species evenness. Patterns at a coarser taxonomic level (genus) mirrored those at the species level. Main conclusion: We show that patterns of variation in plant diversity are dependent on the spatial level considered, but taxonomic level made little difference in understanding these patterns. Therefore, if we do not consider patterns across different spatial levels, important environmental and management drivers could be missed. The high biodiversity value and degree of threat to these European protected habitats makes such understanding invaluable for their conservation

The need of data harmonization to derive robust empirical relationships between soil conditions and vegetation.

Question: Is it possible to improve the general applicability and significance of empirical relationships between abiotic conditions and vegetation by harmonization of temporal data? Location: The Netherlands. Methods: Three datasets of vegetation, recorded after periods with different meteorological conditions, were used to analyze relationships between soil moisture regime (expressed by the mean spring groundwater level - MSLt calculated for different periods) and vegetation (expressed by the mean indicator value for moisture regime Fm). For each releve, measured groundwater levels were interpolated and extrapolated to daily values for the period 1970-2000 by means of an impulse-response model. Sigmoid regression lines between MSLt and Fm were determined for each of the three datasets and for the combined dataset. Results: A measurement period of three years resulted in significantly different relationships between Fm and MSLt for the three datasets (F-test,/? <0.05>. The three regression lines only coincided for the mean spring groundwater level computed over the period 1970-2000 (AfSLclimate) and thus provided a general applicable relationship. Precipitation surplus prior to vegetation recordings strongly affected the relationships. Conclusions: Harmonization of time series data (1) eliminates biased measurements, (2) results in generally applicable relationships between abiotic and vegetation characteristics and (3) increases the goodness of fit of these relationships. The presented harmonization procedure can be used to optimize many relationships between soil and vegetation characteristics. © IAVS; Opulus Press Uppsala

Development of a Boston-area 50-km fiber quantum network testbed

Distributing quantum information between remote systems will necessitate the integration of emerging quantum components with existing communication infrastructure. This requires understanding the channel-induced degradations of the transmitted quantum signals, beyond the typical characterization methods for classical communication systems. Here we report on a comprehensive characterization of a Boston-Area Quantum Network (BARQNET) telecom fiber testbed, measuring the time-of-flight, polarization, and phase noise imparted on transmitted signals. We further design and demonstrate a compensation system that is both resilient to these noise sources and compatible with integration of emerging quantum memory components on the deployed link. These results have utility for future work on the BARQNET as well as other quantum network testbeds in development, enabling near-term quantum networking demonstrations and informing what areas of technology development will be most impactful in advancing future system capabilities.Comment: 9 pages, 5 figures + Supplemental Material

Telecom networking with a diamond quantum memory

Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy (SiV) center in diamond to the telecom O-band, maintaining low noise ($g^2(0)<0.1$) and high indistinguishability ($V=89\pm8\%$). We further demonstrate the utility of this system for quantum networking by converting telecom-band time-bin pulses, sent across a lossy and noisy 50 km deployed fiber link, to the visible band and mapping their quantum states onto a diamond quantum memory with fidelity $\mathcal{F}=87\pm 2.5 \%$. These results demonstrate the viability of SiV quantum memories integrated with telecom-band systems for scalable quantum networking applications.Comment: 9 pages, 5 figures + Supplemental Material

TLS2trees: A scalable tree segmentation pipeline for TLS data

1. Above-ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. 2. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2treesconsists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data cap�tured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. 3. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of se�mantic segmentation step. 4. The volume and scale of TLS data captured in forest plots is increasing. It is sug�gested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software

Sexuality and Body Image After Uterine Artery Embolization and Hysterectomy in the Treatment of Uterine Fibroids: A Randomized Comparison

In this paper the effect of uterine artery embolization (UAE) on sexual functioning and body image is investigated in a randomized comparison to hysterectomy for symptomatic uterine fibroids. The EMbolization versus hysterectoMY (EMMY) trial is a randomized controlled study, conducted at 28 Dutch hospitals. Patients were allocated hysterectomy (n = 89) or UAE (n = 88). Two validated questionnaires (the Sexual Activity Questionnaire [SAQ] and the Body Image Scale [BIS]) were completed by all patients at baseline, 6 weeks, and 6, 12, 18, and 24 months after treatment. Repeated measurements on SAQ scores revealed no differences between the groups. There was a trend toward improved sexual function in both groups at 2 years, although this failed to reach statistical significance except for the dimensions discomfort and habit in the UAE arm. Overall quality of sexual life deteriorated in a minority of cases at all time points, with no significant differences between the groups (at 24 months: UAE, 29.3%, versus hysterectomy, 23.5%; p = 0.32). At 24 months the BIS score had improved in both groups compared to baseline, but the change was only significant in the UAE group (p = 0.009). In conclusion, at 24 months no differences in sexuality and body image were observed between the UAE and the hysterectomy group. On average, both after UAE and hysterectomy sexual functioning and body image scores improved, but significantly so only after UAE

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness