Bose condensates in a harmonic trap near the critical temperature

The mean-field properties of finite-temperature Bose-Einstein gases confined in spherically symmetric harmonic traps are surveyed numerically. The solutions of the Gross-Pitaevskii (GP) and Hartree-Fock-Bogoliubov (HFB) equations for the condensate and low-lying quasiparticle excitations are calculated self-consistently using the discrete variable representation, while the most high-lying states are obtained with a local density approximation. Consistency of the theory for temperatures through the Bose condensation point requires that the thermodynamic chemical potential differ from the eigenvalue of the GP equation; the appropriate modifications lead to results that are continuous as a function of the particle interactions. The HFB equations are made gapless either by invoking the Popov approximation or by renormalizing the particle interactions. The latter approach effectively reduces the strength of the effective scattering length, increases the number of condensate atoms at each temperature, and raises the value of the transition temperature relative to the Popov approximation. The renormalization effect increases approximately with the log of the atom number, and is most pronounced at temperatures near the transition. Comparisons with the results of quantum Monte Carlo calculations and various local density approximations are presented, and experimental consequences are discussed.Comment: 15 pages, 11 embedded figures, revte

Log-moment estimators of the Nakagami-lognormal distribution

[EN] In this paper, estimators of the Nakagami-lognormal (NL) distribution based on the method of log-moments have been derived and thoroughly analyzed. Unlike maximum likelihood (ML) estimators, the log-moment estimators of the NL distribution are obtained using straightforward equations with a unique solution. Also, their performance has been evaluated using the sample mean, confidence regions and normalized mean square error (NMSE). The NL distribution has been extensively used to model composite small-scale fading and shadowing in wireless communication channels. This distribution is of interest in scenarios where the small-scale fading and the shadowing processes cannot be easily separated such as the vehicular environment.This work has been funded in part by the Programa de Estancias de Movilidad de Profesores e Investigadores en Centros Extranjeros de Ensenanza Superior e Investigacion of the Ministerio de Educacion, Cultura y Deporte, Spain, PR2015-00151 and by the Ministerio de Economia, Industria y Competitividad of the Spanish Government under the national project TEC2017-86779-C2-2-R, through the Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER).Reig, J.; Brennan, C.; Rodrigo Peñarrocha, VM.; Rubio Arjona, L. (2019). Log-moment estimators of the Nakagami-lognormal distribution. EURASIP Journal on Wireless Communications and Networking. 1-10. https://doi.org/10.1186/s13638-018-1328-6S110J. M. Ho, G. L. Stüber, in Co-channel interference of microcellular systems on shadowed Nakagami fading channels. Proc. IEEE 43rd Vehicular Technology Conference, 1993 (VTC 93) (IEEESecaucus, 1993), pp. 568–571.A. A. Abu-Dayya, N. C. Beaulieu, Micro- and macrodiversity NCFSK (DPSK) on shadowed Nakagami-fading channels. IEEE Trans. Commun.42(9), 2693–2702 (1994).X. Wang, W. Wang, Z. Bu, Fade statistics for selection diversity in Nakagami-lognormal fading channels. Electron. Lett.42(18), 1046–1047 (2006).D. T. Nguyen, Q. T. Nguyen, S. C. Lam, Analysis and simulation of MRC diversity reception in correlated composite Nakagami-lognormal fading channels. REV J. Electron. Commun.4(1–2), 44–51 (2014).P. Xu, X. Zhou, D. Hu, in Performance evaluations of adaptive modulation over composite Nakagami-lognormal fading channels. 2009 15th Asia-Pacific Conference on Communications (IEEEShanghai, 2009), pp. 467–470.G. C. Alexandropoulos, A. Conti, P. T. Mathiopoulos, in Adaptive M-QAM systems with diversity in correlated Nakagami-m fading and shadowing. IEEE Global Telecommunications Conference (GLOBECOM 2010) (IEEEMiami, 2010), pp. 1–5.Ö. Bulakci, A. B. Saleh, J. Hämäläinen, S. Redana, Performance analysis of relay site planning over composite fading/shadowing channels with cochannel interference. IEEE Trans. Veh. Technol.62(4), 1692–1706 (2013).W. Cheng, Y. Huang, On the performance of adaptive SC/MRC cooperative systems over composite fading channels. Chin. J. Electron.25(3), 533–540 (2016).M. G. Kibria, G. P. Villardi, W. Liao, K. Nguyen, K. Ishizu, F. Kojima, Outage analysis of offloading in heterogeneous networks: Composite fading channels. IEEE Trans. Veh. Technol.66(10), 8990–9004 (2017).K. Cho, J. Lee, C. G. Kang, Stochastic geometry-based coverage and rate analysis under Nakagami & log-normal composite fading channel for downlink cellular networks. IEEE Commun. Lett.21(6), 1437–1440 (2017).R. Singh, M. Rawat, Closed-form distribution and analysis of a combined Nakagami-lognormal shadowing and unshadowing fading channel. J Telecommun. Inf. Technol.4:, 81–87 (2016).J. Reig, L. Rubio, Estimation of the composite fast fading and shadowing distribution using the log-moments in wireless communications. IEEE Trans. Wireless. Commun.12(8), 3672–3681 (2013).S. Atapattu, C. Tellambura, H. Jiang, A mixture gamma distribution to model the SNR of wireless channels. IEEE Trans. Wireless Commun.10(12), 4193–4203 (2011).Q. Wang, H. Lin, P. Kam, Tight bounds and invertible average error probability expressions over composite fading channels. J. Commun. Netw.18(2), 182–189 (2016).J. M. Holtzmann, On using perturbation analysis to do sensitivity analysis: derivatives versus differences. IEEE Trans. Autom. Control. 37(2), 243–247 (1992).H. Suzuki, A statistical model for urban radio propagation. IEEE Trans. Commun.25(7), 673–680 (1977).M. D. Yacoub, The α- μ distribution: a physical fading model for the Stacy distribution. IEEE Trans. Veh. Technol.56(1), 122–124 (2007).P. M. Shankar, Error rates in generalized shadowed fading channels. Wirel. Pers. Commun.28(3), 233–238 (2004).J. -M. Nicolas, Introduction aux statistiques de deuxième espèce: applications des logs-moments et des logs-cumulants à l’analyse des lois d’images radar. Traitement du Signal. 19(3), 139–167 (2002). Translation to English by S. N. Anfinsen.C. Withers, S. Nadarajah, A generalized Suzuki distribution. Wirel. Pers. Commun.62(4), 807–830 (2012).M. Abramowitz, Handbook of Mathematical Functions, with Formulas, Graphs, and Mathematical Tables, 9th edn. (Dover, New York, NY, 1972).M. K. Simon, M. S. Alouini, Digital Communication over Fading Channels, 2nd edn. (Wiley, Hoboken, NY, 2005).Z. Sun, J. Du, in Proc. 10th International Conference, ICIC 2014, ed. by D. -S. Huang, V. Bevilacqua, and P. Premaratne. Log-cumulant parameter estimator of log-normal distribution. Intelligent computing theory (SpringerNew York, NY, 2014), pp. 668–674.S. Zhang, J. M. Jin, Computation of Special Functions (Wiley, New York, 1996).G. Casella, R. L. Berger, Statistical Inference (Duxbury Thomson Learning, Pacific Grove, CA, 2002).C. Kleiber, S. Kotz, Statistical Size Distributions in Economics and Actuarial Sciences (Wiley, Hoboken, NJ, 2003).L. Devroye, Non-uniform Random Variate Generation (Springer, New York,1986).A. Abdi, M. Kaveh, Performance comparison of three different estimators for the Nakagami m parameter using Monte Carlo simulation. IEEE Commun. Lett.4(4), 119–121 (2000).L. Rubio, J. Reig, N. Cardona, Evaluation of Nakagami fading behaviour based on measurements in urban scenarios. Int. J. Electron. Commun. (AEÜ). 61(2), 135–138 (2007)

Identification of New Genes Involved in Human Adipogenesis and Fat Storage

Since the worldwide increase in obesity represents a growing challenge for health care systems, new approaches are needed to effectively treat obesity and its associated diseases. One prerequisite for advances in this field is the identification of genes involved in adipogenesis and/or lipid storage. To provide a systematic analysis of genes that regulate adipose tissue biology and to establish a target-oriented compound screening, we performed a high throughput siRNA screen with primary (pre)adipocytes, using a druggable siRNA library targeting 7,784 human genes. The primary screen showed that 459 genes affected adipogenesis and/or lipid accumulation after knock-down. Out of these hits, 333 could be validated in a secondary screen using independent siRNAs and 110 genes were further regulated on the gene expression level during adipogenesis. Assuming that these genes are involved in neutral lipid storage and/or adipocyte differentiation, we performed InCell-Western analysis for the most striking hits to distinguish between the two phenotypes. Beside well known regulators of adipogenesis and neutral lipid storage (i.e. PPARγ, RXR, Perilipin A) the screening revealed a large number of genes which have not been previously described in the context of fatty tissue biology such as axonemal dyneins. Five out of ten axonemal dyneins were identified in our screen and quantitative RT-PCR-analysis revealed that these genes are expressed in preadipocytes and/or maturing adipocytes. Finally, to show that the genes identified in our screen are per se druggable we performed a proof of principle experiment using an antagonist for HTR2B. The results showed a very similar phenotype compared to knock-down experiments proofing the “druggability”. Thus, we identified new adipogenesis-associated genes and those involved in neutral lipid storage. Moreover, by using a druggable siRNA library the screen data provides a very attractive starting point to identify anti-obesity compounds targeting the adipose tissue

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study

Background: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA.Objective: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA.Methods: Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models.Results: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not.Conclusions: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients.</p

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BackgroundAlthough major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. MethodsEcological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. ResultsOverall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. ConclusionsThe real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood.

BACKGROUND: Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. METHODS: Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. RESULTS: Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. CONCLUSIONS: The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study.

BACKGROUND: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA. OBJECTIVE: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA. METHODS: Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models. RESULTS: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not. CONCLUSIONS: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02542891, https://clinicaltrials.gov/ct2/show/NCT02542891; German Clinical Trials Register, DRKS00006866, https://tinyurl.com/ybja3xz7; Netherlands Trials Register, NTR4962, https://www.trialregister.nl/trial/4838; ClinicalTrials.Gov, NCT02389660, https://clinicaltrials.gov/ct2/show/NCT02389660; ClinicalTrials.gov, NCT02361684, https://clinicaltrials.gov/ct2/show/NCT02361684; ClinicalTrials.gov, NCT02449447, https://clinicaltrials.gov/ct2/show/NCT02449447; ClinicalTrials.gov, NCT02410616, https://clinicaltrials.gov/ct2/show/NCT02410616; ISRCTN registry, ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725