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We present a second-quantization based Liouville-space formulation of response theory for non-eigenstates of unperturbed Hamiltonian in the single-determinant self-consistent field framework, where we include a time-independent relaxation superoperator in the Liouville equation of motion. This density-based formulation uses quantities and concepts similar to those introduced in established wave function-based forms of approximate-state response theory, and we discuss how the wave function-based class of theory relates to the present more general treatment. We also discuss various aspects of the present methodology, including its opportunities and limitations/challenges, and outline future work. [ABSTRACT FROM AUTHOR]

Abstract Introduction Frailty is common in critically ill patients and is associated with increased morbidity and mortality. There remains uncertainty as to the optimal method/timing of frailty assessment and the impact of care processes and adverse events on outcomes is unknown. We conducted a pilot study to inform on the conduct, design and feasibility of a multicenter study measuring frailty longitudinally during critical illness, care processes, occurrence of adverse events, and resultant outcomes. Methods Single-center pilot study enrolling patients over the age of 55 admitted to an Intensive Care Unit (ICU) for life-support interventions including mechanical ventilation, vasopressor therapy and/or renal replacement therapy. Frailty was measured on ICU admission and hospital discharge with the Clinical Frailty Scale (CFS), the Frailty Index (FI) and CFS at 6-month follow-up. Frailty was defined as CFS ≥ 5 and a FI ≥ 0.20. Processes of care and adverse events were measured during their ICU and hospital stay including nutritional support, mobility, nosocomial infections and delirium. ICU, hospital and 6 months were determined. Results In 49 patients enrolled, the mean (SD) age was 68.7 ± 7.9 with a 6-month mortality of 29%. Enrollment was 1 patient/per week. Frailty was successfully measured at different time points during the patients stay/follow-up and varied by method/timing of assessment; by CFS and FI, respectively, in 17/49 (36%), 23/49 (47%) on admission, 22/33 (67%), 21/33 (63%) on hospital discharge and 11/30 (37%) had a CFS ≥ 5 at 6 months. Processes of care and adverse events were readily captured during the ICU and ward stay with the exception of ward nutritional data. ICU, hospital outcomes and follow-up outcomes were worse in those who were frail irrespective of ascertainment method. Pre-existing frailty remained static in survivors, but progressed in non-frail survivors. Discussion In this pilot study, we demonstrate that frailty measurement in critically ill patients over the course and recovery of their illness is feasible, that processes of care and adverse events are readily captured, have developed the tools and obtained data necessary for the planning and conduct of a large multicenter trial studying the interaction between frailty and critical illness.

Androgen receptors are expressed in the kidney and serum testosterone is negatively associated with serum phosphate in males, suggesting a role of testosterone in renal phosphate handling. In this cross‐sectional study, we examined the association of serum total and free testosterone with acute phosphate and calcium excretion in males in response to an oral phosphate challenge. Thirty‐five healthy adult males with normal baseline testosterone levels consumed a 500 mg phosphorus drink and the urinary excretion of minerals, as well as levels of relevant circulating parameters, were assessed at baseline and hourly for 4 h. Serum total testosterone was positively associated with overall phosphate excretion (r = 0.35, p = 0.04) and calcium excretion (r = 0.44, p = 0.00) in response to the challenge. Serum free testosterone was positively associated with post‐challenge calcium excretion (r = 0.34, p = 0.048), but significance was not reached for phosphate excretion (r = 0.31, p = 0.07). Serum total and free testosterone were not associated with parathyroid hormone, fibroblast growth factor‐23, or vitamin D—key factors implicated in phosphate and calcium regulation. Overall, higher serum total testosterone levels in healthy middle‐aged males are associated with a greater capacity to acutely excrete phosphate and calcium after a single oral phosphate challenge, suggesting potential ramifications of testosterone deficiency related to mineral homeostasis. [ABSTRACT FROM AUTHOR]

Context. Species connectivity mapping is a technically challenging task for conservation practitioners and nongovernment organisations to undertake as it requires experience in geographic information systems and often some computer programming. Aims. We developed a decision support tool to provide spatial information and data on potential habitat connectivity and optimum connectivity pathways for a selection of forest-dependent vertebrate fauna in eastern and south-western Australia. Methods. We systematically searched spatial data repositories for Australian spatial datasets for modelling connectivity. A least cost paths and patch connectivity approach was used to map potential habitat connectivity for (1) a single species - the glossy black cockatoo (Calyptorhynchus lathami) of South East Queensland, and (2) four species guilds - rainforest pigeons, gliding possums, the black cockatoos of south-western Western Australia and a landscape level forest connectivity. Optimum connectivity pathways were assessed for protection status. Key results. In total 71 spatial datasets useful for habitat connectivity mapping were identified. Species and guild modelling found that the protection status for optimum connectivity pathways varied between 24.7% and 53.3%. A decision support mapping tool was then created to enable users to interactively explore the connectivity data and download the spatial datasets for further analysis. Conclusions. The development of a decision support tool for mapping habitat connectivity in eastern and south-western Australia represents a useful platform for conservation practitioners as it provides valuable spatial information on potential connectivity pathways for forest-dependent vertebrate fauna. Implications. The tool can aid in the prioritisation of conservation actions aimed at enhancing habitat connectivity and mitigating the impacts of habitat fragmentation on biodiversity in the two regions. [ABSTRACT FROM AUTHOR]

The world's forests are being increasingly disturbed from exposure to the compounding impacts of land use and climate change, in addition to natural disturbance regimes. Boreal forests have a lower level of deforestation compared to tropical forests, and while they have higher levels of natural disturbances, the accumulated impact of forest management for commodity production coupled with worsening fire weather conditions and other climate-related stressors is resulting in ecosystem degradation and loss of biodiversity. We used satellite-based time-series analysis of two canopy indices—canopy photosynthesis and canopy water stress—to calculate an index that maps the relative stability of forest canopies in the Canadian provinces of Ontario and Quebec. By drawing upon available spatial time-series data on logging, wildfire, and insect infestation impacts, we were able to attribute the causal determinants of areas identified as having unstable forest canopy. The slope of the two indices that comprise the stability index also provided information as to where the forest is recovering from prior disturbances. The stability analyses and associated spatial datasets are available in an interactive web-based mapping app. that can be used to map disturbed forest canopies and the attribution of disturbances to human or natural causes. This information can assist decision-makers in identifying areas that are potentially ecologically degraded and in need of restoration and those stable areas that are a priority for protection. [ABSTRACT FROM AUTHOR]

In the research field, smartphone began to be used for field data collection. One application that was used was KoBoCollect. This article shared the experience of using KoBoCollect in research on the socio-economic impact of blood diseases on banana in Sumba Island. The research was conducted by interviewing 64 banana farmers that spread proportionally in four regencies in Sumba Island. The use of KoBoCollect/toolbox made it easy to collect, save and manage research data. KoBoCollect/toolbox provided the advantages of conducting paperless research, able to record data in complex formats such as numeric data, descriptive data, photos, videos, sounds, coordinates and other data, dynamically making forms changes when conducting research, and displaying interactive applications. KoBoCollect can be run off-line, made it possible to collect field data in remote locations and no internet network. The research data was transmitted online so that the data tabulation process was automatic. Simple data analysis such as median values, mode, graphs and distribution maps of data collection were provided in KoBotoolbox. With KoBoCollect/toolbox, researchers quickly find out the results of the research, as in this research it was known that blood disease had an impact on the decrease in the banana population revealed by 81.54% of farmers.

We present an implementation for the calculation of molecular response properties using the algebraic-diagrammatic construction (ADC)/intermediate state representation approach. For the second-order ADC model [ADC(2)], a memory-efficient ansatz avoiding the storage of double excitation amplitudes is investigated. We compare the performance of different numerical algorithms for the solution of the underlying response equations for ADC(2) and show that our approach also strongly improves the convergence behavior for the investigated algorithms compared with the standard implementation. All routines are implemented in an open-source Python library. [ABSTRACT FROM AUTHOR]

The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survival time or its distribution or their transformations through a function of a linear regression form. In this article, we propose nonparametric, nonlinear algorithms (deepAFT methods) based on deep artificial neural networks to model survival outcome data in the broad distribution family of accelerated failure time models. The proposed methods predict survival outcomes directly and tackle the problem of censoring via an imputation algorithm as well as re‐weighting and transformation techniques based on the inverse probabilities of censoring. Through extensive simulation studies, we confirm that the proposed deepAFT methods achieve accurate predictions. They outperform the existing regression models in prediction accuracy, while being flexible and robust in modeling covariate effects of various nonlinear forms. Their prediction performance is comparable to other established deep learning methods such as deepSurv and random survival forest methods. Even though the direct output is the expected survival time, the proposed AFT methods also provide predictions for distributional functions such as the cumulative hazard and survival functions without additional learning efforts. For situations where the popular Cox regression model may not be appropriate, the deepAFT methods provide useful and effective alternatives, as shown in simulations, and demonstrated in applications to a lymphoma clinical trial study. [ABSTRACT FROM AUTHOR]