Search

Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project “SmartNIALMeter” brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance's consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data.

Increasing demands for limited natural resources accelerate rethinking their usage and processing. A focal point of interest lies in the steel industry, given its substantial contribution to emissions and the notable attribute of steel being fully recyclable. Hence, closing material loops to ensure the preservation of material value and supply security is of substantial importance. Yet, until today, supply chains are still characterized by interrupted information streams that prevent circular material flows. Digital platforms are attributed to overcoming these shortcomings due to their ability to moderate ecosystems and render technological connectivity. However, industrial companies lack knowledge of how digital platforms can be used to design closed material loops. Therefore, this paper is built on a longitudinal case study of a joint venture between a recycling and steel melting plant company aiming at creating a digital platform to close the material flow of steel. Six design priorities, structured along the three core building blocks of digital platform ecosystems and the four core dimensions of collective action theory (CAT), have been derived to guide managers in designing digital platforms for the CE-specific context. This study presently contributes to understanding the relationship between digital platforms and CE.

Abstract Background Persons with spinal cord injury (SCI) living in the community often require care. The boundaries between professional home care and informal care are blurred, and it is unclear who the typical user of home care is. The objective of this study was to describe the characteristics of persons with SCI using professional home care in Switzerland, determine the frequency of home care visits, and investigate the association of sociodemographic factors, SCI-specific characteristics, secondary health conditions, and functional independence with the use of home care. Methods We used cross-sectional data from the 2017 community survey of the Swiss Spinal Cord Injury Cohort Study (SwiSCI). Out of 3,959 eligible individuals 1294 completed the questionnaire and were included in the analysis (response rate 33%). Using descriptive statistics, differences between home care users and non-users as well as the frequency of home care visits were investigated. The association between sociodemographic factors, SCI-specific characteristics, secondary health conditions, functional independence and the use of home care was analyzed using multivariable logistic regression. Multiple imputation was used to account for missing data. Results Of 1,294 participants, 280 (22%) used professional home care. The median weekly professional home care duration was 6 h (Q1 = 2, Q3 = 12). More home care was used in persons with lower functional independence (Odds ratio (OR) 0.30 per 10 unit decrease in the Spinal Cord Independence Measure, 95%-Confidence interval (CI) 0.24–0.37), fewer secondary health conditions (OR 0.96 per unit Spinal Cord Injury Secondary Conditions Scale, 95%-CI 0.94–0.99), tetraplegia (OR 2.77, 95%-CI 1.92–4.00), women (OR 2.42, 95%-CI 1.70–3.43), higher age (OR 1.22 per 10 years increase, 95%-CI 1.06–1.39), living alone (OR 2.48, 95%-CI 1.53–4.03), and those receiving support from an informal caregiver (OR 1.88, 95%-CI 1.27–2.77). Conclusions This is the first study to examine the use of professional home care from the perspective of persons with SCI in Switzerland. Lower functional independence strongly predicts increased home care use. The findings showed that professional home care complements informal care and is more likely to be used by individuals with SCI who live alone, have tetraplegia, and are female.

ABSTRACTDuring the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent—something that is unknown at the time of sequencing—and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses.

Introduction Patients with Multiple Sclerosis (pwMS) treated with anti-CD20 (cluster of differentiation) monoclonal antibodies (mAbs) such as ocrelizumab (OCR) and ofatumumab (OFA) show a reduction mainly of B-lymphocytes, but also other lymphocyte subsets can be affected by these treatments. There is limited data on differences between lymphocyte subset counts of pwMS after treatment initiation with OCR or OFA. Objective To compare lymphocyte subset counts after treatment initiation in pwMS treated with OCR and OFA. Methods We analyzed 22 pwMS initiated on OFA and 56 sex-, age- and MS course matched pwMS initiated on OCR from 2 prospectively collected observational MS databases (Bern [n: OFA 14, OCR 44] and Vienna [n: OFA 8, OCR 12]) statistically comparing lymphocyte subset counts (Mann Whitney Test). Results We found that pwMS treated with OCR showed a stronger reduction of CD20 B-lymphocytes ( P = .001), and a trend towards lower counts of CD8 + T cells ( P = .056) compared to pwMS treated with OFA, whereas reduction of total lymphocyte, CD4 + lymphocyte and NK cell count was equally distributed between both treatments. Conclusion Different effects on lymphocyte subpopulations appear to be present in pwMS after treatment initiation with different anti-CD20 mAbs. Further studies are needed to determine potential effects on anti-CD20 treatment efficacy as well as treatment associated risks such as failed vaccinations and infections.

Background: Reporting of sex-specific analyses in multiple sclerosis (MS) is sparse. Disability accrual results from relapses (relapse-associated worsening) and independent thereof (progression independent of relapses). Objectives: A population of MS patients during relapse treated per standard of care was analyzed for sex differences and short-term relapse outcome (3–6 months) as measured by Expanded Disability Status Scale (EDSS) change. Design: Single-center retrospective study. Methods: We analyzed 134 MS relapses between March 2016 and August 2020. All events required relapse treatment (steroids and/or plasma exchange). Demographic, disease, and paraclinical characteristics [cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI)] were displayed separated by sex. Multivariable linear regression was run to identify factors associated with short-term EDSS change. Results: Mean age at relapse was 38.4 years (95% confidence interval: 36.3–40.4) with a proportion of 71.6% women in our cohort. Smoking was more than twice as prevalent in men (65.8%) than women (32.3%). In- and after-relapse EDSSs were higher in men [men: 3.3 (2.8–3.9), women: 2.7 (2.4–3.0); men: 3.0 (1.3–3.6); women: 1.8 (1.5–2.1)] despite similar relapse intervention. Paraclinical parameters revealed no sex differences. Our primary model identified female sex, younger age, and higher EDSS at relapse to be associated with EDSS improvement. A higher immunoglobulin G (IgG) quotient (CSF/serum) was associated with poorer short-term outcome [mean days between first relapse treatment and last EDSS assessment 130.2 (79.3–181.0)]. Conclusion: Sex and gender differences are important in outcome analyses of MS relapses. Effective treatment regimens need to respect putative markers for a worse outcome to modify long-term prognosis such as clinical and demographic variables, complemented by intrathecal IgG synthesis. Prospective trials should be designed to address these differences and confirm our results.

A large number of disease-modifying immunotherapies are available for the treatment of people with multiple sclerosis. Many disease-modifying immunotherapies show scarce or no safety data in pregnancy and breastfeeding and are labeled as being contraindicated during these periods in the Swiss summary of product characteristics. Some disease-modifying immunotherapies also have restrictions for male patients. Hence, family planning should always be considered in treatment decisions. If clinically necessary, the continuation of immunotherapy during pregnancy can be considered for some substances. In these situations, the “Good Off-Label Use Practice”, careful consideration of the benefit–risk profile, and interprofessional cooperation between the treating neurologist, obstetrician–gynecologist, and pharmacist/pharmacologist, ideally with the involvement of experienced centers, is necessary. Here, we present an update on disease-modifying immunotherapies in multiple sclerosis with a focus on family planning, pregnancy, and breastfeeding and provide consensus recommendations of the Medico-Scientific Advisory Board of the Swiss Multiple Sclerosis Society, the Swiss Neurological Society, and the Swiss Society for Gynecology and Obstetrics (represented by the Academy of Fetomaternal Medicine). These unified national recommendations are necessary, as guidelines from other countries differ and because of separate approval/reimbursement situations in Switzerland.

Abstract T helper 9 (TH9) cells promote allergic tissue inflammation and express the type 2 cytokines, IL-9 and IL-13, as well as the transcription factor, PPAR-γ. However, the functional role of PPAR-γ in human TH9 cells remains unknown. Here, we demonstrate that PPAR-γ drives activation-induced glycolysis, which, in turn, promotes the expression of IL-9, but not IL-13, in an mTORC1-dependent manner. In vitro and ex vivo experiments show that the PPAR-γ-mTORC1-IL-9 pathway is active in TH9 cells in human skin inflammation. Additionally, we find dynamic regulation of tissue glucose levels in acute allergic skin inflammation, suggesting that in situ glucose availability is linked to distinct immunological functions in vivo. Furthermore, paracrine IL-9 induces expression of the lactate transporter, MCT1, in TH cells and promotes their aerobic glycolysis and proliferative capacity. Altogether, our findings uncover a hitherto unknown relationship between PPAR-γ-dependent glucose metabolism and pathogenic effector functions in human TH9 cells.

There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions.