Photodetachment study of He^- quartet resonances below the He(n=3) thresholds

The photodetachment cross section of He^- has been measured in the photon energy range 2.9 eV to 3.3 eV in order to investigate doubly excited states. Measurements were made channel specific by selectively detecting the residual He atoms left in a particular excited state following detachment. Three Feshbach resonances were found in the He(1s2p ^3P)+e^-(epsilon p) partial cross section: a ^4S resonance below the He(1s3s ^3S) threshold and two ^4P resonances below the He(1s3p ^3P) threshold. The measured energies of these doubly excited states are 2.959260(6) eV, 3.072(7) eV and 3.26487(4) eV. The corresponding widths are found to be 0.20(2) meV, 50(5) meV and 0.61(5) meV. The measured energies agree well with recent theoretical predictions for the 1s3s4s ^4S, 1s3p^2 ^4P and 1s3p4p ^4P states, respectively, but the widths deviate noticeably from calculations for 1s3p^2 ^4P and 1s3p4p ^4P states.Comment: 10 pages, 3 figures, LaTeX2e scrartcl, amsmath. Accepted by Journal of Physics B; minor changes after referee repor

Collimated dual species oven source and its characterisation via spatially resolved fluorescence spectroscopy

We describe the design, construction and characterisation of a collimated, dual-species oven source for generating intense beams of lithium and caesium in UHV environments. Our design produces full beam overlap for the two species. Using an aligned microtube array the FWHM of the output beam is restricted to ~ 75 milliradians, with an estimated axial brightness of 3.6x10[superscript]14 atoms s[superscript]-1 sr[superscript]-1 for Li and 7.4x10[superscript]15 atoms s[superscript]-1 sr[superscript]-1 for Cs. We measure the properties of the output beam using a spatially-resolved fluorescence technique, which allows for the extraction of additional information not accessible without spatial resolution

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomized control trials

Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety co-morbidity, and consider the association of a wide range of symptoms with treatment outcomes. / Method: Individual patient data from six RCTs of depressed patients (total n=2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention timepoints using individual items and sum scores. Symptom networks (Graphical Gaussian Model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators. / Results: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms. / Conclusion: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

BACKGROUND: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. METHOD: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. RESULTS: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). CONCLUSIONS: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments

Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

Aims: To develop, validate, and compare the performance of nine models predicting post-treatment outcomes for depressed adults based on pre-treatment data. / Methods: Individual patient data from all six eligible RCTs were used to develop (k=3, n=1722) and test (k=3, n=1136) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum-scores were developed using coefficient weights derived from network centrality statistics (Models 1-3) and factor loadings from a confirmatory factor analysis (Model 4). Unweighted sum-score models were tested using Elastic Net Regularized (ENR) and ordinary least squares (OLS) regression (Models 5-6). Individual items were then included in ENR and OLS (Models 7-8). All models were compared to one another and to a null model using the mean post-baseline BDI-II score in the training data (Model 9). Primary outcome: BDI-II scores at 3-4 months. / Results: Models 1-7 all outperformed the null model. Individual-item models (particularly Model 8) explained less variance. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum-scores had little impact. / Conclusions: Any of the modelling techniques (1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression

Life events and treatment prognosis for depression: A systematic review and individual patient data meta-analysis

Objective: To investigate associations between major life events and prognosis independent of treatment type: (1) after adjusting for clinical prognostic factors and socio-demographics; (2) amongst patients with depressive episodes at least six-months long; and (3) patients with a first life-time depressive episode. // Methods: Six RCTs of adults seeking treatment for depression in primary care met eligibility criteria, individual patient data (IPD) were collated from all six (n = 2858). Participants were randomized to any treatment and completed the same baseline assessment of life events, demographics and clinical prognostic factors. Two-stage random effects meta-analyses were conducted. // Results: Reporting any major life events was associated with poorer prognosis regardless of treatment type. Controlling for baseline clinical factors, socio-demographics and social support resulted in minimal residual evidence of associations between life events and treatment prognosis. However, removing factors that might mediate the relationships between life events and outcomes reporting: arguments/disputes, problem debt, violent crime, losing one's job, and three or more life events were associated with considerably worse prognoses (percentage difference in 3–4 months depressive symptoms compared to no reported life events =30.3%(95%CI: 18.4–43.3)). // Conclusions: Assessing for clinical prognostic factors, social support, and socio-demographics is likely to be more informative for prognosis than assessing self-reported recent major life events. However, clinicians might find it useful to ask about such events, and if they are still affecting the patient, consider interventions to tackle problems related to those events (e.g. employment support, mediation, or debt advice). Further investigations of the efficacy of such interventions will be important

Photodetachment study of the 1s3s4s ^4S resonance in He^-

A Feshbach resonance associated with the 1s3s4s ^{4}S state of He^{-} has been observed in the He(1s2s ^{3}S) + e^- (\epsilon s) partial photodetachment cross section. The residual He(1s2s ^{3}S) atoms were resonantly ionized and the resulting He^+ ions were detected in the presence of a small background. A collinear laser-ion beam apparatus was used to attain both high resolution and sensitivity. We measured a resonance energy E_r = 2.959 255(7) eV and a width \Gamma = 0.19(3) meV, in agreement with a recent calculation.Comment: LaTeX article, 4 pages, 3 figures, 21 reference

Predicting prognosis for adults with depression using individual symptom data:a comparison of modelling approaches

BACKGROUND: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. METHODS: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. RESULTS: Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. CONCLUSIONS: Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression

New hydroxylated metabolites of 4-monochlorobiphenyl in whole poplar plants

Two new monohydroxy metabolites of 4-monochlorobiphenyl (CB3) were positively identified using three newly synthesized monohydroxy compounds of CB3: 2-hydroxy-4-chlorobiphenyl (2OH-CB3), 3-hydroxy-4-chlorobiphenyl (3OH-CB3) and 4-hydroxy-3-chlorobiphenyl (4OH-CB2). New metabolites of CB3, including 2OH-CB3 and 3OH-CB3, were confirmed in whole poplars (Populus deltoides × nigra, DN34), a model plant in the application of phytoremediation. Furthermore, the concentrations and masses of 2OH-CB3 and 3OH-CB3 formed in various tissues of whole poplar plants and controls were measured. Results showed that 2OH-CB3 was the major product in these two OH-CB3s with chlorine and hydroxyl moieties in the same phenyl ring of CB3. Masses of 2OH-CB3 and 3OH-CB3 in tissues of whole poplar plants were much higher than those in the hydroponic solution, strongly indicating that the poplar plant itself metabolizes CB3 to both 2OH-CB3 and 3OH-CB3. The total yield of 2OH-CB3 and 3OH-CB3, with chlorine and hydroxyl in the same phenyl ring of CB3, was less than that of three previously found OH-CB3s with chlorine and hydroxyl in the opposite phenyl rings of CB3 (2'OH-CB3, 3'OH-CB3, and 4'OH-CB3). Finally, these two newly detected OH-CB3s from CB3 in this work also suggests that the metabolic pathway was via epoxide intermediates. These five OH-CB3s clearly showed the complete metabolism profile from CB3 to monohydroxylated CB3. More importantly, it's the first report and confirmation of 2OH-CB3 and 3OH-CB3 (new metabolites of CB3) in a living organism

Multi-channel analog of the effective-range expansion

Similarly to the standard effective range expansion that is done near the threshold energy, we obtain a generalized power-series expansion of the multi-channel Jost-matrix that can be done near an arbitrary point on the Riemann surface of the energy within the domain of its analyticity. In order to do this, we analytically factorize its momentum dependencies at all the branching points on the Riemann surface. The remaining single-valued matrix functions of the energy are then expanded in the power-series near an arbitrary point in the domain of the complex energy plane where it is analytic. A systematic and accurate procedure has been developed for calculating the expansion coefficients. This means that near an arbitrary point in the domain of physically interesting complex energies it is possible to obtain a semi-analytic expression for the Jost-matrix (and therefore for the S-matrix) and use it, for example, to locate the spectral points (bound and resonant states) as the S-matrix poles.Comment: 33 pages, 10 figure