Multi-perspective embedding for non-metric time series classification

The interest in time series analysis is rapidly increasing, providing new challenges for machine learning. Over many decades, Dynamic Time Warping (DTW) is referred to as the de facto standard distance measure for time series and the tool of choice when analyzing such data. Nevertheless, DTW has two major drawbacks: (a) it is non-metric and therefore hard to handle by standard machine learning techniques, and (b) it is not well suited for multi-dimensional time series. For this purpose, we propose a multi-perspective embedding of the time series into a complex-valued vector space and the evaluation by a model that is able to handle complex-valued data. The approach is evaluated on various multi-dimensional time series data and with different classifier techniques

Scalable embedding of multiple perspectives for indefinite life-science data analysis

Life science data analysis frequently encounters particular challenges that cannot be solved with classical techniques from data analytics or machine learning domains. The complex inherent structure of the data and especially the encoding in non-standard ways, e.g., as genome- or protein-sequences, graph structure or histograms, often limit the development of appropriate classification models. To address these limitations, the application of domain-specific expert similarity measures has gained a lot of attention in the past. However, the use of such expert measures suffers from two major drawbacks: (a) there is not one outstanding similarity measure that guarantees success in all application scenarios, and (b) such similarity functions often lead to indefinite data that cannot be processed by classical machine learning methods. In order to tackle both of these limitations, this paper presents a method to embed indefinite life science data with various similarity measures at the same time into a complex-valued vector space. We test our approach on various life science data sets and evaluate the performance against other competitive methods to show its efficiency

Excess resource use and costs of physical comorbidities in individuals with mental health disorders: A systematic literature review and meta-analysis

Individuals with mental health disorders (MHDs) have worse physical health than the general population, utilise healthcare resources more frequently and intensively, incurring higher costs. We provide a first comprehensive overview and quantitative synthesis of literature on the mag- nitude of excess resource use and costs for those with MHDs and comorbid physical health condi- tions (PHCs). This systematic review (PROSPERO CRD42017075319) searched studies comparing resource use or costs of individuals with MHDs and comorbid PHCs versus individuals without co- morbid conditions published between 2007 and 2021. We conducted narrative and quantitative syntheses, using random-effects meta-analyses to explore ranges of excess resource use and costs across care segments, comparing to MHD only, PHC only, or general population controls (GPC). Of 20,075 records, 228 and 100 were eligible for narrative and quantitative syntheses, respectively. Most studies were from the US, covered depression or schizophrenia, reporting endocrine/metabolic or circulatory comorbidities. Frequently investigated healthcare segments were inpatient, outpatient, emergency care and medications. Evidence on lost productivity, long-term and informal care was rare. Substantial differences exist between MHDs, with depressive disorder tending towards lower average excess resource use and cost estimates, while excess resource use ranges between +6% to +320% and excess costs between +14% to +614%. PHCs are major drivers of resource use and costs for individuals with MHDs, affecting care segments differently. Significant physical health gains and cost savings are potentially achievable through prevention, earlier identification, management and treatment, using more integrated care approaches. Current international evidence, however, is heterogeneous with limited geographical representativeness and comparability

Quasi-elastic polarization-transfer measurements on the deuteron in anti-parallel kinematics

We present measurements of the polarization-transfer components in the $^2$H$(\vec e,e'\vec p)$ reaction, covering a previously unexplored kinematic region with large positive (anti-parallel) missing momentum, $p_{\rm miss}$, up to 220 MeV$/c$, and $Q^2=0.65$ $({\rm GeV}/c)^2$. These measurements, performed at the Mainz Microtron (MAMI), were motivated by theoretical calculations which predict small final-state interaction (FSI) effects in these kinematics, making them favorable for searching for medium modifications of bound nucleons in nuclei. We find in this kinematic region that the measured polarization-transfer components $P_x$ and $P_z$ and their ratio agree with the theoretical calculations, which use free-proton form factors. Using this, we establish upper limits on possible medium effects that modify the bound proton's form factor ratio $G_E/G_M$ at the level of a few percent. We also compare the measured polarization-transfer components and their ratio for $^2$H to those of a free (moving) proton. We find that the universal behavior of $^2$H, $^4$He and $^{12}$C in the double ratio $\frac{(P_x/P_z)^A}{(P_x/P_z)^{^1\rm H}}$ is maintained in the positive missing-momentum region

SARS-CoV-2 mutations affect antigen processing by the proteasome to alter CD8+ T cell responses

Mutations within viral epitopes can result in escape from T cells, but the contribution of mutations in flanking regions of epitopes in SARS-CoV-2 has not been investigated. Focusing on two SARS-CoV-2 nucleoprotein CD8+ epitopes, we investigated the contribution of these flanking mutations to proteasomal processing and T cell activation. We found decreased NP9-17-B*27:05 CD8+ T cell responses to the NP-Q7K mutation, likely due to a lack of efficient epitope production by the proteasome, suggesting immune escape caused by this mutation. In contrast, NP-P6L and NP-D103 N/Y mutations flanking the NP9-17-B*27:05 and NP105-113-B*07:02 epitopes, respectively, increased CD8+ T cell responses associated with enhanced epitope production by the proteasome. Our results provide evidence that SARS-CoV-2 mutations outside the epitope could have a significant impact on proteasomal processing, either contributing to T cell escape or enhancement that may be exploited for future vaccine design

Time-Independent Gravitational Fields

This article reviews, from a global point of view, rigorous results on time independent spacetimes. Throughout attention is confined to isolated bodies at rest or in uniform rotation in an otherwise empty universe. The discussion starts from first principles and is, as much as possible, self-contained.Comment: 47 pages, LaTeX, uses Springer cl2emult styl

Effects of add-on Celecoxib treatment on patients with schizophrenia spectrum disorders and inflammatory cytokine profile trial (TargetFlame): study design and methodology of a multicentre randomized, placebo-controlled trial

Neuroinflammation has been proposed to impact symptomatology in patients with schizophrenia spectrum disorders. While previous studies have shown equivocal effects of treatments with add-on anti-inflammatory drugs such as Aspirin, N-acetylcysteine and Celecoxib, none have used a subset of prospectively recruited patients exhibiting an inflammatory profile. The aim of the study is to evaluate the efficacy and safety as well as the cost-effectiveness of a treatment with 400 mg Celecoxib added to an ongoing antipsychotic treatment in patients with schizophrenia spectrum disorders exhibiting an inflammatory profile. The “Add-on Celecoxib treatment in patients with schizophrenia spectrum disorders and inflammatory cytokine profile trial (TargetFlame)” is a multicentre randomized, placebo-controlled phase III investigator-initiated clinical trial with the following two arms: patients exhibiting an inflammatory profile receiving either add-on Celecoxib 400 mg/day or add-on placebo. A total of 199 patients will be assessed for eligibility by measuring blood levels of three pro-inflammatory cytokines, and 109 patients with an inflammatory profile, i.e. inflamed, will be randomized, treated for 8 weeks and followed-up for additional four months. The primary endpoint will be changes in symptom severity as assessed by total Positive and Negative Syndrome Scale (PANSS) score changes from baseline to week 8. Secondary endpoints include various other measures of psychopathology and safety. Additional health economic analyses will be performed. TargetFlame is the first study aimed at evaluating the efficacy, safety and cost-effectiveness of the antiphlogistic agent Celecoxib in a subset of patients with schizophrenia spectrum disorders exhibiting an inflammatory profile. With TargetFlame, we intended to investigate a novel precision medicine approach towards anti-inflammatory antipsychotic treatment augmentation using drug repurposing. Clinical trial registration: http://www.drks.de/DRKS00029044 and https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS0002904

Modeling the release of Escherichia coli from soil into overland flow under raindrop impact

Pathogen transport through the environment is complicated, involving a variety of physical, chemical, and biological processes. This study considered the transfer of microorganisms from soil into overland flow under rain-splash conditions. Although microorganisms are colloidal particles, they are commonly quantified as colony-forming units (CFUs) per volume rather than as a mass or number of particles per volume, which poses a modeling challenge. However, for very small particles that essentially remain suspended after being ejected into ponded water and for which diffusion can be neglected, the Gao model, originally derived for solute transfer from soil, describes particle transfer into suspension and is identical to the Hairsine–Rose particle erosion model for this special application. Small-scale rainfall experiments were conducted in which an Escherichia coli (E. coli) suspension was mixed with a simple soil (9:1 sand-to-clay mass ratio). The model fit the experimental E. coli data. Although re-conceptualizing the Gao solute model as a particle suspension model was convenient for accommodating the unfortunate units of CFU ml−1, the Hairsine–Rose model is insensitive to assumptions about E. coli per CFU as long as the assumed initial mass concentration of E. coli is very small compared to that of the soil particle classes. Although they undoubtedly actively interact with their environment, this study shows that transport of microorganisms from soil into overland storm flows can be reasonably modeled using the same principles that have been applied to small mineral particles in previous studies

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications