Explore the vast range of titles available.

Purpose For patients with cancer of unknown primary (CUP), treatment options are limited. Precision oncology, the interplay of comprehensive genomic profiling (CGP) and targeted therapies, aims to offer additional treatment options to patients with advanced and hard-to-treat cancers. We aimed to highlight the use of a molecular tumor board (MTB) in the therapeutic management of CUP patients. Methods In this single-center observational study, CUP patients, presented to the MTB of the Comprehensive Cancer Center Munich LMU, a tertiary care center, were analyzed retrospectively. Descriptive statistics were applied to describe relevant findings. Results Between June 2016 and February 2022, 61 patients with unfavorable CUP were presented to the MTB, detected clinically relevant variants in 74% (45/61) of patients, of which 64% (29/45) led to therapeutic recommendation. In four out of 29 patients (14%), the treatment recommendations were implemented, unfortunately without resulting in clinical benefit. Reasons for not following the therapeutic recommendation were mainly caused by the physicians’ choice of another therapy (9/25, 36%), especially in the context of worsening of general condition, lost to follow-up (7/25, 28%) and death (6/25, 24%). Conclusion CGP and subsequent presentation to a molecular tumor board led to a high rate of therapeutic recommendations in patients with CUP. Recommendations were only implemented at a low rate; however, late GCP diagnostic and, respectively, MTB referral were found more frequent for the patients with implemented treatment. This contrast underscores the need for early implementation of CGP into the management of CUP patients.

The multi-variate optimal fingerprint method for the detection of an externally forced climate change signal in the presence of natural internal variability is extended to the attribution problem. To determine whether a climate change signal which has been detected in observed climate data can be attributed to a particular climate forcing mechanism, or combination of mechanisms, the predicted space-time dependent climate change signal patterns for the candidate climate forcings must be specified. In addition to the signal patterns, the method requires input information on the space-time dependent covariance matrices of the natural climate variability and of the errors of the predicted signal patterns. The detection and attribution problem is treated as a sequence of individual consistency tests applied to all candidate forcing mechanisms, as well as to the null hypothesis that no climate change has taken place, within the phase space spanned by the predicted climate change patterns. As output the method yields a significance level for the detection of a climate change signal in the observed data and individual confidence levels for the consistency of the retrieved climate change signal with each of the forcing mechanisms. A statistically significant climate change signal is regarded as consistent with a given forcing mechanism if the statistical confidence level exceeds a given critical value, but is attributed to that forcing only if all other candidate climate change mechanisms (from a finite set of proposed mechanisms) are rejected at that confidence level. Although all relations can be readily expressed in standard matrix notation, the analysis is carried out using tensor notation, with a metric given by the natural-variability covariance matrix. This simplifies the derivations and clarifies the invariant relation between the covariant signal patterns and their contravariant fingerprint counterparts. The signal patterns define the reduced vector space in which the climate trajectories are analyzed, while the fingerprints are needed to project the climate trajectories onto this reduced space.[PUBLICATION ABSTRACT]

An overview is given of a classical unified theory of gravity, elementary particles and quantum phenomena based on soliton solutions of Einstein's vacuum equations in twelve dimensional space. Bell's theorem on the Einstein-Podolsky-Rosen experiment, which is widely interpreted as ruling out classical explanations of quantum phenomena, is shown to be non-applicable as violating time-reversal symmetry. Entanglement is a relativistic consequence of Newon's third law and a property of all time-symmetrical theories, whether classical or quantal. The metric solitons (metrons) are composed of strongly nonlinear periodic core components, far fields corresponding to the classical gravitational and electromagnetic far fields of point-like particles, and further fields representing the weak and strong interactions. The core fields represent nonlinear eigenmodes trapped in a self-generated wave guide. Computations are presented for the first family of elementary particles corresponding to the lowest nonlinear eigenmodes; the second and third families are assumed to correspond to higher eigenmodes. It is shown that the periodicities of the soliton core modes produce the wave-particle duality paradoxes of quantum phenomena, as exemplified by single- and double-slit particle diffraction and the discrete structure of atomic spectra.

Purpose Early clinical trials are the first step into clinical therapies for new drugs. Within the six Bavarian university-based hospitals (Augsburg, Erlangen, Regensburg, Munich (LMU and TU), Würzburg) we have enrolled a virtual network platform for patient discussion. Methods The virtual Early Clinical Trial Unit Tumor Board (ECTU Tumor Board) is a secured web-based meeting to evaluate early clinical trial options for patients, where representatives from local ECTUs participate. We retrospectively analyzed patient cases discussed between November 2021 and November 2022. Results From November 2021 to November 2022, a total of 43 patients were discussed in the ECTU Tumor Board. Median age at diagnosis was 44.6 years (range 10–76 years). The median number of previous lines of therapies was 3.7 (range 1–9 therapies) including systemic treatment, surgery, and radiation therapy. A total of 27 different tumor entities were presented and 83.7% (36/43) patients received at least one trial recommendation. In total, 21 different active or shortly recruiting clinical trials were recommended: ten antibody trials, four BiTE (bispecific T cell engager) trials, six CAR (chimeric antigen receptor) T-cell trials, and one chemotherapy trial. Only six trials (28.6%) were recommended on the basis of the previously performed comprehensive genetic profiling (CGP). Conclusion The ECTU Tumor Board is a feasible and successful network, highlighting the force of virtual patient discussions for improving patient care as well as trial recruitment in advanced diseases. It can provide further treatment options after local MTB presentation, aiming to close the gap to access clinical trials.

An optimal linear filter (fingerprint) is derived for the detection of a given time-dependent, multivariate climate change signal in the presence of natural climate variability noise. Application of the fingerprint to the observed (or model simulated) climate data yields a climate change detection variable (detector) with maximal signal-to-noise ratio. The optimal fingerprint is given by the product of the assumed signal pattern and the inverse of the climate variability covariance matrix. The data can consist of any, not necessarily dynamically complete, climate dataset for which estimates of the natural variability covariance matrix exist. The single-pattern analysis readily generalizes to the multipattern case of a climate change signal lying in a prescribed (in practice relatively low dimensional) signal pattern space: the single-pattern result is simply applied separately to each individual base pattern spanning the signal pattern space. Multipattern detection methods can be applied either to test the statistical significance of individual components of a predicted multicomponent climate change response, using separate single-pattern detection tests, or to determine the statistical significance of the complete signal, using a multivariate test. Both detection modes make use of the same set of detectors. The difference in direction of the assumed signal pattern and computed optimal fingerprint vector allows alternative interpretations of the estimated signal associated with the set of optimal detectors. The present analysis yields an estimated signal lying in the assumed signal space, whereas an earlier analysis of the time-independent detection problem by Hasselmann yielded an estimated signal in the computed fingerprint space. The different interpretations can be explained by different choices of the metric used to relate the signal space to the fingerprint space (inverse covariance matrix versus standard Euclidean metric, respectively). Two simple natural variability models are considered: a space–time separability model, and an expansion in terms of POPs ( principal oscillation patterns ). For each model the application of the optimal fingerprint method is illustrated by an example.

A new generation of integrated assessment models of climate change policies is needed to capture the basic dynamical processes that govern the required transformation of the present fossil-based global economic system to a sustainable decarbonized system. After an overview of the abatement technologies and policy instruments that are already available and able today to achieve the transformation, three examples are presented of typical actor-based, system-dynamical models that are able to simulate some of the key dynamics of the transition processes. In addition to developing a new hierarchy of integrated assessment models, scientists need also to better educate the public and policy makers on the wide-reaching implications of the inherent inertia of the climate system.

In the conventional approach to the detection of an anthropogenic or other externally forced climate change signal, optimal filters (fingerprints) are used to maximize the ratio of the observed climate change signal to the natural variability noise. If detection is successful, attribution of the observed climate change to the hypothesized forcing mechanism is carried out in a second step by comparing the observed and predicted climate change signals. In contrast, the Bayesian approach to detection and attribution makes no distinction between detection and attribution. The purpose of filtering in this case is to maximize the impact of the evidence, the observed climate change, on the prior probability that the hypothesis of an anthropogenic origin of the observed signal is true. Whereas in the conventional approach model uncertainties have no direct impact on the definition of the optimal detection fingerprint, in optimal Bayesian filtering they play a central role. The number of patterns retained is governed by the magnitude of the predicted signal relative to the model uncertainties, defined in a pattern space normalized by the natural climate variability. Although this results in some reduction of the original phase space, this is not the primary objective of Bayesian filtering, in contrast to the conventional approach, in which dimensional reduction is a necessary prerequisite for enhancing the signal-to-noise ratio. The Bayesian filtering method is illustrated for two anthropogenic forcing hypotheses: greenhouse gases alone, and a combination of greenhouse gases plus sulfate aerosols. The hypotheses are tested against 31-year trends for near-surface temperature, summer and winter diurnal temperature range, and precipitation. Between six and thirteen response patterns can be retained, as compared with the one or two response patterns normally used in the conventional approach. Strong evidence is found for the detection of an anthropogenic climate change in temperature, with some preference given to the combined forcing hypothesis. Detection of recent anthropogenic trends in diurnal temperature range and precipitation is not successful, but there remains strong net evidence for anthropogenic climate change if all data are considered jointly.[PUBLICATION ABSTRACT]

Impulse-response-function (IRF) models are designed for applications requiring a large number of climate change simulations, such as multi-scenario climate impact studies or cost-benefit integrated-assessment studies. The models apply linear response theory to reproduce the characteristics of the climate response to external forcing computed with sophisticated state-of-the-art climate models like general circulation models of the physical ocean-atmosphere system and three-dimensional oceanic-plus-terrestrial carbon cycle models. Although highly computer efficient, IRF models are nonetheless capable of reproducing the full set of climate-change information generated by the complex models against which they are calibrated. While limited in principle to the linear response regime (less than about 3^sup ^C global-mean temperature change), the applicability of the IRF model presented has been extended into the nonlinear domain through explicit treatment of the climate system's dominant nonlinearities: CO^sub 2^ chemistry in ocean water, CO^sub 2^ fertilization of land biota, and sublinear radiative forcing. The resultant nonlinear impulse-response model of the coupled carbon cycle-climate system (NICCS) computes the temporal evolution of spatial patterns of climate change for four climate variables of particular relevance for climate impact studies: near-surface temperature, cloud cover, precipitation, and sea level. The space-time response characteristics of the model are derived from an EOF analysis of a transient 850-year greenhouse warming simulation with the Hamburg atmosphere-ocean general circulation model ECHAM3-LSG and a similar response experiment with the Hamburg carbon cycle model HAMOCC. The model is applied to two long-term CO^sub 2^ emission scenarios, demonstrating that the use of all currently estimated fossil fuel resources would carry the Earth's climate far beyond the range of climate change for which reliable quantitative predictions are possible today, and that even a freezing of emissions to present-day levels would cause a major global warming in the long term.[PUBLICATION ABSTRACT]

Continuing a discussion on the intertemporal accounting of climate-change damages initiated by Nordhaus, Heal and Brown in response to the recent demonstration of Hasselmann et al. that standard exponential discounting applied uniformly to all goods and services invariably leads to a 'climate catastrophe' in cost-benefit analyses, it is argued that (1) there exists no economically satisfactory alternative to cost-benefit analysis for the determination of optimal climate protection strategies.

A structurally highly simplified, globally integrated coupled climate-economic costs model is used to compute optimal paths of global CO2 emissions that minimize the net sum of climate damage and mitigation costs.