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\"At the core of Einstein's general theory of relativity are a set of equations that explain the relationship among gravity, space, and time--possibly the most perfect intellectual achievement of modern physics. For over a century, physicists have been exploring, debating, and at times neglecting Einstein's theory in their quest to uncover the history of the universe, the origin of time, and the evolution of solar systems, stars, and galaxies. In this sweeping narrative of science and culture, Pedro Ferreira explains the theory through the human drama surrounding it: the personal feuds and intellectual battles of the biggest names in twentieth-century physics, from Einstein and Eddington to Hawking and Penrose. We are in the midst of a momentous transformation in modern physics. As scientists look farther and more clearly into space than ever before, The Perfect Theory engagingly reveals the greater relevance of general relativity, showing us where it started, where it has led, and where it can still take us\"-- Provided by publisher.

Grazing exclusion may lead to biodiversity loss and homogenization of naturally heterogeneous and species-rich grassland ecosystems, and these effects may cascade to higher trophic levels and ecosystem properties. Although grazing exclusion has been studied elsewhere, the consequences of alleviating the disturbance regime in grassland ecosystems remain unclear. In this paper, we present results of the first five years of an experiment in native grasslands of southern Brazil. Using a randomized block experimental design, we examined the effects of three grazing treatments on plant and arthropod communities: (i) deferred grazing (i.e., intermittent grazing), (ii) grazing exclusion and (iii) a control under traditional continuous grazing, which were applied to 70 x 70 m experimental plots, in six regionally distributed blocks. We evaluated plant community responses regarding taxonomic and functional diversity (life-forms) in separate spatial components: alpha (1 x 1 m subplots), beta, and gamma (70 x 70 m plots), as well as the cascading effects on arthropod high-taxa. By estimating effect sizes (treatments vs. control) by bootstrap resampling, both deferred grazing and grazing exclusion mostly increased vegetation height, plant biomass and standing dead biomass. The effect of grazing exclusion on plant taxonomic diversity was negative. Conversely, deferred grazing increased plant taxonomic diversity, but both treatments reduced plant functional diversity. Reduced grazing pressure in both treatments promoted the break of dominance by prostrate species, followed by fast homogenization of vegetation structure towards dominance of ligneous and erect species. These changes in the plant community led to increases in high-taxa richness and abundance of vegetation-dwelling arthropod groups under both treatments, but had no detectable effects on epigeic arthropods. Our results indicate that decision-making regarding the conservation of southern Brazil grasslands should include both intensive and alleviated levels of grazing management, but not complete grazing exclusion, to maximize conservation results when considering plant and arthropod communities.

A bstract We present a comprehensive study on Higgs pair production in various archetypical extended Higgs sectors such as the real and the complex 2-Higgs-Doublet Model, the 2-Higgs-Doublet Model augmented by a real singlet field and the Next-to-Minimal Supersymmetric extension of the Standard Model. We take into account all relevant theoretical and experimental constraints, in particular the experimental limits on non-resonant and resonant Higgs pair production. We present the allowed cross sections for Standard Model (SM)-like Higgs pair production and the ranges of the SM-like Yukawa and trilinear Higgs self-coupling that are still compatible with the applied constraints. Furthermore, we give results for the pair production of a SM-like with a non-SM-like Higgs boson and for the production of a pair of non-SM-like Higgs bosons. We find that di-Higgs production in the models under investigation can exceed the SM rate substantially, not only in the non-resonance region but also due to resonant enhancement. We give several benchmarks with interesting features such as large cross sections, the possibility to test CP violation, Higgs-to-Higgs cascade decays or di-Higgs production beating single Higgs production. In all of our benchmark points, the next-to-leading order QCD corrections are included in the large top-mass limit. For these points, we found that, depending on the model and the Higgs pair final state, the corrections increase the leading order cross section by a factor of 1.79 to 2.24. We also discuss the relation between the description of Higgs pair production in an effective field theory approach and in the specific models investigated here.

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact—limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R 2 ) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations—ECFP4 fingerprints increased R 2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R 2 ) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R 2 ) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Both DAPA-HF (assessing dapagliflozin) and EMPEROR-Reduced (assessing empagliflozin) trials showed that sodium-glucose co-transporter-2 (SGLT2) inhibition reduced the combined risk of cardiovascular death or hospitalisation for heart failure in patients with heart failure with reduced ejection fraction (HFrEF) with or without diabetes. However, neither trial was powered to assess effects on cardiovascular death or all-cause death or to characterise effects in clinically important subgroups. Using study-level published data from DAPA-HF and patient-level data from EMPEROR-Reduced, we aimed to estimate the effect of SGLT2 inhibition on fatal and non-fatal heart failure events and renal outcomes in all randomly assigned patients with HFrEF and in relevant subgroups from DAPA-HF and EMPEROR-Reduced trials. We did a prespecified meta-analysis of the two single large-scale trials assessing the effects of SGLT2 inhibitors on cardiovascular outcomes in patients with HFrEF with or without diabetes: DAPA-HF (assessing dapagliflozin) and EMPEROR-Reduced (assessing empagliflozin). The primary endpoint was time to all-cause death. Additionally, we assessed the effects of treatment in prespecified subgroups on the combined risk of cardiovascular death or hospitalisation for heart failure. These subgroups were based on type 2 diabetes status, age, sex, angiotensin receptor neprilysin inhibitor (ARNI) treatment, New York Heart Association (NYHA) functional class, race, history of hospitalisation for heart failure, estimated glomerular filtration rate (eGFR), body-mass index, and region (post-hoc). We used hazard ratios (HRs) derived from Cox proportional hazard models for time-to-first event endpoints and Cochran's Q test for treatment interactions; the analysis of recurrent events was based on rate ratios derived from the Lin-Wei-Yang-Ying model. Among 8474 patients combined from both trials, the estimated treatment effect was a 13% reduction in all-cause death (pooled HR 0·87, 95% CI 0·77–0·98; p=0·018) and 14% reduction in cardiovascular death (0·86, 0·76–0·98; p=0·027). SGLT2 inhibition was accompanied by a 26% relative reduction in the combined risk of cardiovascular death or first hospitalisation for heart failure (0·74, 0·68–0·82; p<0·0001), and by a 25% decrease in the composite of recurrent hospitalisations for heart failure or cardiovascular death (0·75, 0·68–0·84; p<0·0001). The risk of the composite renal endpoint was also reduced (0·62, 0·43–0·90; p=0·013). All tests for heterogeneity of effect size between trials were not significant. The pooled treatment effects showed consistent benefits for subgroups based on age, sex, diabetes, treatment with an ARNI and baseline eGFR, but suggested treatment-by-subgroup interactions for subgroups based on NYHA functional class and race. The effects of empagliflozin and dapagliflozin on hospitalisations for heart failure were consistent in the two independent trials and suggest that these agents also improve renal outcomes and reduce all-cause and cardiovascular death in patients with HFrEF. Boehringer Ingelheim.

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes–which is most clearly seen in blood–though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.

We here report on the progress of the HHH Workshop, that took place in Dubrovnik in July 2023. After the discovery of a particle that complies with the properties of the Higgs boson of the Standard Model, all Standard Model (SM) parameters are in principle determined. However, in order to verify or falsify the model, the full form of the potential has to be determined. This includes the measurement of the triple and quartic scalar couplings. We here report on ongoing progress of measurements for multi-scalar final states, with an emphasis on three SM-like scalar bosons at 125 Ge V , but also mentioning other options. We discuss both experimental progress and challenges as well as theoretical studies and models that can enhance such rates with respect to the SM predictions.

In this work, we resolve conflicting experimental and theoretical findings related to the dynamical stability and superconducting properties of F m 3 ¯ m -LuH 3 , which was recently suggested as the parent phase harboring room-temperature superconductivity at near-ambient pressures. Including temperature and quantum anharmonic lattice effects in our calculations, we demonstrate that the theoretically predicted structural instability of the F m 3 ¯ m phase near ambient pressures is suppressed for temperatures above 200 K. We provide a p – T phase diagram for stability up to pressures of 6 GPa, where the required temperature for stability is reduced to T  > 80 K. We also determine the superconducting critical temperature T c of F m 3 ¯ m -LuH 3 within the Migdal-Eliashberg formalism, using temperature- and quantum-anharmonically-corrected phonon dispersions, finding that the expected T c for electron-phonon mediated superconductivity is in the range of 50–60 K, i.e., well below the temperatures required to stabilize the lattice. When considering moderate doping based on rigidly shifting the Fermi level, T c decreases for both hole and electron doping. Our results thus provide evidence that any observed room-temperature superconductivity in pure or doped F m 3 ¯ m -LuH 3 , if confirmed, cannot be explained by a conventional electron-phonon mediated pairing mechanism. Superconductivity was recently reported experimentally in nitrogen-doped lutetium hydride with Tc = 294 K at 1 GPa. Here, via theoretical calculations taking into account temperature and quantum anharmonic lattice effects, the authors find that room-temperature superconductivity in the suggested parent phase of LuH 3 cannot be explained by a conventional electron-phonon mediated pairing mechanism.

In public health context, oncology is associated with severe negative impact on patients and on their relatives’ quality of life. Over the last decades, survival has remained at 50% worldwide for some tumor locations. Patient reported outcomes (PROs) assessment and, the corresponding use in clinical practice, help establishing patient individualized profiling involving caregivers. The purpose of this systematic review was to examine critical success factors for PROs assessment in daily clinical oncology practice. Additionally, we investigated how PROs collection can change oncology perspectives for patients and caregivers. According to PRISMA guidelines, 83 studies were included in this systematic review, whether related with implementation in daily clinical practice or associated with its use in oncology. PROs assessment gathers multi-professional teams, biomedical and clinical expertise, patients, families and caregivers. Institutional involvement, first line for caregiver’s adherence, team continuous formation, encompassing training and support, design of clear workflows, continuous monitoring, and data analysis are crucial for implementation. PROs measures are decisive in oncology. Several items were improved, including caregiver–patient–physician communication, patient risk groups identification, unmet problems and needs detection, disease course and treatment tracking, prognostic markers, cost-effectiveness measurement and comfort/support provision for both patients and caregivers. Routine assessment and implementation of PROs in clinical practice are a major challenge and a paradigm transformation for future.