How multisensory neurons solve causal inference.

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference

Improvement of pain and psychological status after six months of psychotherapy in cancer patients

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Epigenetic therapies for heart failure: Current insights and future potential

Despite the current reductionist approach providing an optimal indication for diagnosis and treatment of patients with heart failure with reduced ejection fraction (HFrEF), there are no standard pharmacological therapies for heart failure with preserved ejection fraction (HFpEF). Although in its infancy in cardiovascular diseases, the epigenetic-based therapy (“epidrugs”) is capturing the interest of physician community. In fact, an increasing number of controlled clinical trials is evaluating the putative beneficial effects of: 1) direct epigenetic-oriented drugs, eg, apabetalone, and 2) repurposed drugs with a possible indirect epigenetic interference, eg, metformin, statins, sodium glucose transporter inhibitors 2 (SGLT2i), and omega 3 polyunsaturated fatty acids (PUFAs) in both HFrEF and HFpEF, separately. Apabetalone is the first and unique direct epidrug tested in cardiovascular patients to date, and the BETonMACE trial has reported a reduction in first HF hospitalization (any EF value) and cardiovascular death in patients with type 2 diabetes and recent acute coronary syndrome, suggesting a possible role in secondary prevention. Patients with HFpEF seem to benefit from supplementation to the standard therapy with statins, metformin, and SGLT2i owing to their ability in reducing mortality. In contrast, the vasodilator hydralazine, with or without isosorbide dinitrate, did not provide beneficial effects. In HFrEF, metformin and SGLT2i could reduce the risk of incident HF and mortality in affected patients whereas clinical trials based on statins provided mixed results. Furthermore, PUFAs diet supplementation was significantly associated with reduced cardiovascular risk in both HFpEF and HFrEF. Future large trials will reveal whether direct and indirect epitherapy will remain a work in progress or become a useful way to customize the therapy in the real-world management of HFpEF and HFrEF. Our goal is to discuss the recent advancement in the epitherapy as a possible way to improve personalized therapy of HF

High-dose chemotherapy as initial salvage treatment in relapsed testicular cancer patients

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Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

Abstract. The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data

Prognostic role of circulating cytokines and inflammation indexes for avelumab maintenance in metastatic urothelial carcinoma

Background: Avelumab maintenance after first-line platinum-based chemotherapy represents a cornerstone for the treatment of metastatic urothelial carcinoma (mUC). However, identifying prognostic biomarkers is paramount for optimizing patients’ benefits while minimizing toxicity. Cytokines represent circulating mediators of the complex interaction between cancer, the immune system, and inflammation. Inflammation, a hallmark of cancer, can be expressed by circulating factors. In different tumor subtypes, peripheral blood biomarkers, such as circulating cytokines, and systemic inflammatory indexes, have been addressed as potential prognostic factors for immune checkpoint inhibitors. However, their role in mUC still needs to be determined. Methods: Between February 2021 and April 2023, we prospectively collected plasma cytokines and inflammation indexes in 28 patients with mUC before starting avelumab as first-line maintenance. The primary endpoint was the relationship between baseline cytokines and inflammatory indexes with the clinical benefit (CB), defined as the number of Responders. Secondary endpoints included the correlation of baseline cytokines and inflammatory indexes with progression-free survival (PFS), overall survival (OS), and the number and grade of immune-related adverse events. Results: High pre-treatment levels of interferon (IFN)-γ and interleukin (IL)-2, and low levels of IL-6, IL-8, neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and systemic-inflammation index (SII) were associated with clinical benefit and longer survival. In the multivariate analysis, low IL-8, NLR, and SII levels maintained a positive prognostic value for OS. Conclusion: Our data suggest that, in mUC patients receiving avelumab, pre-treatment levels of plasma cytokines and inflammatory indexes may serve as potential prognostic biomarkers for response and efficacy. In particular, patients with signs of pre-therapeutic inflammation showed a significantly lower response and survival to avelumab. On the contrary, low systemic inflammation and high levels of cytokines characterized responders and longer survivors

Correlation between diastolic impairment and lipid metabolism in mild-to-moderate hypertensive postmenopausal women1

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Reflectivity and velocity radar data assimilation for two flash flood events in central Italy: A comparison between 3D and 4D variational methods

The aim of this study is to provide an evaluation of the impact of two largely used data assimilation techniques, namely three- and four-dimensional variational data assimilation systems (3D-Var and 4D-Var), on the forecasting of heavy precipitation events using the Weather Research and Forecasting (WRF) model. For this purpose, two flash flood events in central Italy are analysed. The first occurred on September 14, 2012 during an Intensive Observation Period of the Hydrological cycle in the Mediterranean experiment (HyMeX) campaign, while the other occurred on May 3, 2018. Radial velocity and reflectivity acquired by C-band weather radars at Mt. Midia (central Italy) and San Pietro Capofiume (northern Italy), as well as conventional observations (SYNOP and TEMP), are assimilated into the WRF model to simulate these damaging flash flood events. In order to evaluate the impact of the 3D-Var and 4D-Var assimilation systems on the estimation of short-term quantitative precipitation forecasts, several experiments are carried out using conventional observations with and without radar data. Rainfall evaluation is performed by means of point-by-point and filtering methodologies. The results point to a positive impact of the 4D-Var technique compared to results without assimilation and with 3D-Var experiments. More specifically, the 4D-Var system produces an increase of up to 22% in terms of the Fractions Skill Score compared to 3D-Var for the first flash flood event, while an increase of about 5% is achieved for the second event. The use of a warm start initialization results in a considerable reduction in the spin-up time and a significant improvement in the rainfall forecast, suggesting that the initial precipitation spin-up problem still occurs when using 4D-Var