Quality of Life in Systemic Lupus Erythematosus and Other Chronic Diseases: Highlighting the Amplified Impact of Depressive Episodes

Background: Extensive research has explored SLE’s impact on health-related quality of life (H-QoL), especially its connection with mental wellbeing. Recent evidence indicates that depressive syndromes significantly affect H-QoL in SLE. This study aims to quantify SLE’s impact on H-QoL, accounting for comorbid depressive episodes through case-control studies. Methods: A case-control study was conducted with SLE patients (meeting the ACR/EULAR 2019 criteria of age ≥ 18). The control group was chosen from a community database. H-QoL was measured with the SF-12 questionnaire, and PHQ-9 was used to assess depressive episodes. Results: SLE significantly worsened H-QoL with an attributable burden of 5.37 ± 4.46. When compared to other chronic diseases, only multiple sclerosis had a worse impact on H-QoL. Major depressive episodes had a significant impact on SLE patients’ H-QoL, with an attributable burden of 9.43 ± 5.10, similar to its impact on solid cancers but greater than its impact on other diseases. Conclusions: SLE has a comparable impact on QoL to serious chronic disorders. Concomitant depressive episodes notably worsened SLE patients’ QoL, exceeding other conditions, similar to solid tumors. This underscores the significance of addressing mood disorders in SLE patients. Given the influence of mood disorders on SLE outcomes, early identification and treatment are crucial

Nonorthogonal Multiple Access and Subgrouping for Improved Resource Allocation in Multicast 5G NR

The ever-increasing demand for applications with stringent constraints in device density, latency, user mobility, or peak data rate has led to the appearance of the last generation of mobile networks (i.e., 5G). However, there is still room for improvement in the network spectral efficiency, not only at the waveform level but also at the Radio Resource Management (RRM). Up to now, solutions based on multicast transmissions have presented considerable efficiency increments by successfully implementing subgrouping strategies. These techniques enable more efficient exploitation of channel time and frequency resources by splitting users into subgroups and applying independent and adaptive modulation and coding schemes. However, at the RRM, traditional multiplexing techniques pose a hard limit in exploiting the available resources, especially when users' QoS requests are unbalanced. Under these circumstances, this paper proposes jointly applying the subgrouping and Non-Orthogonal Multiple Access (NOMA) techniques in 5G to increase the network data rate. This study shows that NOMA is highly spectrum-efficient and could improve the system throughput performance in certain conditions. In the first part of this paper, an in-depth analysis of the implications of introducing NOMA techniques in 5G subgrouping at RRM is carried out. Afterward, the validation is accomplished by applying the proposed approach to different 5G use cases based on vehicular communications. After a comprehensive analysis of the results, a theoretical approach combining NOMA and time division is presented, which improves considerably the data rate offered in each use case.This work was supported in part by the Italian Ministry of University and Research (MIUR), within the Smart Cities framework, Project Cagliari2020 ID: PON04a2_00381; in part by the Basque Government under Grant IT1234-19; and in part by the Spanish Government [Project PHANTOM under Grant RTI2018-099162-B-I00 (MCIU/AEI/FEDER, UE)]

In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and repeatability assessment

Background: The human gleno-humeral joint is normally represented as a spherical hinge and its center of rotation is used to construct humerus anatomical axes and as reduction point for the computation of the internal joint moments. The position of the gleno-humeral joint center (GHJC) can be estimated by recording ad hoc shoulder joint movement following a functional approach. In the last years, extensive research has been conducted to improve GHJC estimate as obtained from positioning systems such as stereo-photogrammetry or electromagnetic tracking. Conversely, despite the growing interest for wearable technologies in the field of human movement analysis, no studies investigated the problem of GHJC estimation using miniaturized magneto-inertial measurement units (MIMUs). The aim of this study was to evaluate both accuracy and precision of the GHJC estimation as obtained using a MIMU-based methodology and a functional approach. Methods: Five different functional methods were implemented and comparatively assessed under different experimental conditions (two types of shoulder motions: cross and star type motion; two joint velocities: ωmax = 90°/s, 180°/s; two ranges of motion: Θ = 45°, 90°). Validation was conducted on five healthy subjects and true GHJC locations were obtained using magnetic resonance imaging. Results: The best performing methods (NAP and SAC) showed an accuracy in the estimate of the GHJC between 20.6 and 21.9 mm and repeatability values between 9.4 and 10.4 mm. Methods performance did not show significant differences for the type of arm motion analyzed or a reduction of the arm angular velocity (180°/s and 90°/s). In addition, a reduction of the joint range of motion (90° and 45°) did not seem to influence significantly the GHJC position estimate except in a few subject-method combinations. Conclusions: MIMU-based functional methods can be used to estimate the GHJC position in vivo with errors of the same order of magnitude than those obtained using traditionally stereo-photogrammetric techniques. The methodology proposed seemed to be robust under different experimental conditions. The present paper was awarded as "SIAMOC Best Methodological Paper 2016"

Integral MRAC with Minimal Controller Synthesis and bounded adaptive gains: The continuous-time case

Model reference adaptive controllers designed via the Minimal Control Synthesis (MCS) approach are a viable solution to control plants affected by parameter uncertainty, unmodelled dynamics, and disturbances. Despite its effectiveness to impose the required reference dynamics, an apparent drift of the adaptive gains, which can eventually lead to closed-loop instability or alter tracking performance, may occasionally be induced by external disturbances. This problem has been recently addressed for this class of adaptive algorithms in the discrete-time case and for square-integrable perturbations by using a parameter projection strategy [1]. In this paper we tackle systematically this issue for MCS continuous-time adaptive systems with integral action by enhancing the adaptive mechanism not only with a parameter projection method, but also embedding a s-modification strategy. The former is used to preserve convergence to zero of the tracking error when the disturbance is bounded and L2, while the latter guarantees global uniform ultimate boundedness under continuous L8 disturbances. In both cases, the proposed control schemes ensure boundedness of all the closed-loop signals. The strategies are numerically validated by considering systems subject to different kinds of disturbances. In addition, an electrical power circuit is used to show the applicability of the algorithms to engineering problems requiring a precise tracking of a reference profile over a long time range despite disturbances, unmodelled dynamics, and parameter uncertainty.Postprint (author's final draft

Modelling dust extinction in the Magellanic Clouds

We model the extinction profiles observed in the Small and Large Magellanic clouds with a synthetic population of dust grains consisting by core-mantle particles and a collection of free-flying polycyclic aromatic hydrocarbons. All different flavors of the extinction curves observed in the Magellanic Clouds can be described by the present model, that has been previously (successfully) applied to a large sample of diffuse and translucent lines of sight in the Milky Way. We find that in the Magellanic Clouds the extinction produced by classic grains is generally larger than absorption by polycyclic aromatic hydrocarbons. Within this model, the non-linear far-UV rise is accounted for by polycyclic aromatic hydrocarbons, whose presence in turn is always associated to a gap in the size distribution of classical particles. This hints either a physical connection between (e.g., a common cause for) polycyclic aromatic hydrocarbons and the absence of middle-sized dust particles, or the need for an additional component in the model, that can account for the non-linear far-UV rise without contributing to the UV bump at $\sim$217 nm, e.g., nanodiamonds

Measurement of the Ratio Gamma(KL -> pi+ pi-)/Gamma(KL -> pi e nu) and Extraction of the CP Violation Parameter |eta+-|

We present a measurement of the ratio of the decay rates Gamma(KL -> pi+ pi-)/Gamma(KL -> pi e nu), denoted as Gamma(K2pi)/Gamma(Ke3). The analysis is based on data taken during a dedicated run in 1999 by the NA48 experiment at the CERN SPS. Using a sample of 47000 K2pi and five million Ke3 decays, we find Gamma(K2pi)/Gamma(Ke3) = (4.835 +- 0.022(stat) +- 0.016(syst)) x 10^-3. From this we derive the branching ratio of the CP violating decay KL -> pi+ pi- and the CP violation parameter |eta+-|. Excluding the CP conserving direct photon emission component KL -> pi+ pi- gamma, we obtain the results BR(KL -> pi+ pi-) = (1.941 +- 0.019) x 10^-3 and |eta+-| = (2.223 +- 0.012) x 10^-3.Comment: 20 pages, 7 figures, accepted by Phys. Lett.

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan