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Snow is highly sensitive to atmospheric warming. However, because of the lack of sufficiently long snow avalanche time series and statistical techniques capable of accounting for the numerous biases inherent to sparse and incomplete avalanche records, the evolution of process activity in a warming climate remains little known. Filling this gap requires innovative approaches that put avalanche activity into a long-term context. Here, we combine extensive historical records and Bayesian techniques to construct a 240-y chronicle of snow avalanching in the Vosges Mountains (France). We show evidence that the transition from the late Little Ice Age to the early twentieth century (i.e., 1850 to 1920 CE) was not only characterized by local winter warming in the order of +1.35 °C but that this warming also resulted in a more than sevenfold reduction in yearly avalanche numbers, a severe shrinkage of avalanche size, and shorter avalanche seasons as well as in a reduction of the extent of avalanche-prone terrain. Using a substantial corpus of snow and climate proxy sources, we explain this abrupt shift with increasingly scarcer snow conditions with the low-to-medium elevations of the Vosges Mountains (600 to 1,200 m above sea level [a.s.l.]). As a result, avalanches migrated upslope, with only a relict activity persisting at the highest elevations (release areas >1,200 m a.s.l.). This abrupt, unambiguous response of snow avalanche activity to warming provides valuable information to anticipate likely changes in avalanche behavior in higher mountain environments under ongoing and future warming.

Nanoparticle technology in cancer chemotherapy is a promising approach to enhance active ingredient pharmacology and pharmacodynamics. Indeed, drug nanoparticles display various assets such as extended blood lifespan, high drug loading and reduced cytotoxicity leading to better drug compliance. In this context, organic nanocrystal suspensions for pharmaceutical use have been developed in the past ten years. Nanocrystals offer new possibilities by combining the nanoformulation features with the properties of solid dispersed therapeutic ingredients including (i) high loading of the active ingredient, (ii) its bioavailability improvement, and (iii) reduced drug systemic cytotoxicity. However, surprisingly, no antitumoral drug has been marketed as a nanocrystal suspension until now. Etoposide, which is largely used as an anti-cancerous agent against testicular, ovarian, small cell lung, colon and breast cancer in its liquid dosage form, has been selected to develop injectable nanocrystal suspensions designed to be transferred to the clinic. The aim of the present work is to provide optimized formulations for nanostructured etoposide solutions and validate by means of in vitro and in vivo evaluations the efficiency of this multiphase system. Indeed, the etoposide formulated as a nanosuspension by a bottom-up approach showed higher blood life span, reduced tumor growth and higher tolerance in a murine carcinoma cancer model. The results obtained are promising for future clinical evaluation of these etoposide nanosuspensions.

BackgroundThe sense of time continuity appears to be disturbed in pathologies like schizophrenia, associated with a disruption of the sense of self, and of the feeling of being immersed in the world. Prediction mechanisms have been proposed to be involved in the sense of time continuity by helping to relate discontinuous events, and our previous studies have suggested that these mechanisms may occur even at the millisecond level. Such mechanisms would be involved in our ability to interact with the outer world, by helping to follow events accurately in both space and time. We explored prediction mechanisms and attention shifts based on recently experienced sequences of visual information (sequential effects).Methods26 patients with schizophrenia and 20 matched controls were included in asynchrony discrimination tasks, and assessments of minimal self disorders with the EASE. On each trial, two stimuli are displayed with varying stimulus onset asynchronies (SOA). Subjects decide whether the stimuli are simultaneous or asynchronous and give manual responses. Sequential effects were explored further in several groups of 15 healthy participants.ResultsThe main finding in controls is a strong accuracy advantage for different- as compared to same-order trials, but only when trial t is with an SOA slightly larger than trial t-1 (advantage of 16% in accuracy), or equivalent (advantage of 10% in accuracy). An advantage for same-vs. different-order trials is observed only when the SOA on the previous trial is large and visible. In patients, there is no advantage for same-order trials. There is a clear advantage for different-order trials (advantage of 16% in accuracy), but this effect disappears, contrary to controls, when SOAs are equivalent on successive trials (1% difference in accuracy). The impairment in the trial-to-trial effects in patients correlates with minimal self disorders (the EASE).DiscussionFurther investigations in healthy participants suggest that the sequential effects can be explained in terms of prediction of stimulus sequences from trial to trial, which are accompanied by an attention shift. The first stimulus triggers the onset of the sequence, and attention is then covertly shifted in space and time according to the previous trial, in order to attend to the second stimulus. This explains the advantage for different-order trials: when order is reversed on the present trial, attention ends up in the location of the first stimulus of the present sequence. This first stimulus is perceived as isolated on the screen if the second stimulus occurs later than on the previous trial, thus facilitating the detection of an asynchrony. The asynchrony is then obvious, explaining the large amplitude of the effect. The fact that the effect extends to the condition in which SOAs are equivalent on successive trials suggests that participants shift their attention in advance, as if anticipating the location of the second stimulus. This is impaired in patients, who replay sequences of events, but do not anticipate the successive events flexibly. This would impair their immersion in the world, where events rarely happen twice at the same time exactly.

For years, phenomenological psychiatry has proposed that distortions of the temporal structure of consciousness contribute to the abnormal experiences described before schizophrenia emerges, and may relate to basic disturbances in consciousness of the self. However, considering that temporality refers mainly to an implicit aspect of our relationship with the world, disturbances in the temporal structure of consciousness remain difficult to access. Nonetheless, previous studies have shown a correlation between self disorders and the automatic ability to expect an event in time, suggesting timing is a key issue for the psychopathology of schizophrenia. Timing disorders may represent a target for cognitive remediation, but this requires that disorders can be demonstrated at an individual level. Since cognitive impairments in patients with schizophrenia are discrete, and there is no standardized timing exploration, we focused on timing impairments suggested to be related to self disorders. We present the case report of AF, a 22 year old man suffering from schizophrenia, with no antipsychotic intake. Although AF shows few positive and negative symptoms and has a normal neurocognitive assessment, he shows a high level of disturbance of Minimal Self Disorders (SDs) (assessed with the EASE scale). Moreover, AF has a rare ability to describe his self and time difficulties. An objective assessment of timing ability (variable foreperiod task) confirmed that AF had temporal impairments similar to those previously described in patients, i.e., a preserved ability to distinguish time intervals, but a difficulty to benefit from the passage of time to expect a visual stimulus. He presents additional difficulties in benefitting from temporal cues and adapting to changes in time delays. The impairments were ample enough to yield significant effects with analyses at the individual level. Although causal relationships between subjective and objective impairments cannot be established, the results show that exploring timing deficits at the individual level is possible in patients with schizophrenia. Besides, the results are consistent with hypotheses relating minimal self disorders (SDs) to timing difficulties. They suggest that both subjective and objective timing investigations should be developed further so that their use at an individual level can be generalized in clinical practice.

Patients with schizophrenia have difficulty in making sensory predictions, in the time domain, which have been proposed to be related to self-disorders. However experimental evidence is lacking. We examined both voluntary and automatic forms of temporal prediction in 28 patients and 24 matched controls. A visual cue predicted (temporal cue) or not (neutral cue) the time (400 ms/1000 ms) at which a subsequent target was presented. In both patients and controls, RTs were faster for targets presented after long versus short intervals due to the temporal predictability inherent in the elapse of time (“hazard function”). This RT benefit was correlated with scores on the EASE scale, which measures disorders of the self: patients with a high ‘self-awareness and presence’ score did not show any significant benefit of the hazard function, whereas this ability was preserved in patients with a low score. Moreover, all patients were abnormally sensitive to the presence of “catch” trials (unexpected absence of a target) within a testing block, with RTs actually becoming slower at long versus short intervals. These results indicate fragility in patients’ ability to continuously extract temporally predictive information from the elapsing interval. This deficit might contribute to perturbations of the minimal self in patients.

Acute exercise induces transient modifications in the tumor microenvironment and has been linked to reduced tumor growth along with increased infiltration of immune cells within the tumor in mouse models. In this study, we aimed to evaluate the impact of acute exercise before treatment administration on tumor growth in a mice model of MC38 colorectal cancer receiving an immune checkpoint inhibitor (ICI) and chemotherapy. Six-week-old mice injected with colorectal cancer cells (MC38) were randomized in 4 groups: control (CTRL), immuno-chemotherapy (TRT), exercise (EXE) and combined intervention (TRT/EXE). Both TRT and TRT-EXE received ICI: anti-PD1-1 (1 injection/week) and capecitabine + oxaliplatin (5 times a week) for 1 week (experimentation 1), 3 weeks (experimentation 2). TRT-EXE and EXE groups were submitted to 50 minutes of treadmill exercise before each treatment administration. Over the protocol duration, tumor size has been monitored daily. Tumor growth and microenvironment parameters were measured after the intervention on Day 7 (D7) and Day 16 (D16). From day 4 to day 7, tumor volumes decreased in the EXE/TRT group while remaining stable in the TRT group (p=0.0213). From day 7 until day 16 tumor volume decreased with no significant difference between TRT and TRT/EXE. At D7 the TRT/EXE group exhibited a higher total infiltrate T cell (p=0.0118) and CD8+ cytotoxic T cell (p=0.0031). At D16, tumor marker of apoptosis, vascular integrity and inflammation were not significantly different between TRT and TRT/EXE. Our main result was that acute exercise before immuno-chemotherapy administration significantly decreased early-phase tumor growth (D0 to D4). Additionally, exercise led to immune cell infiltration changes during the first week after exercise, while no significant molecular alterations in the tumor were observed 3 weeks after exercise.

This paper proposes to solve the vortex gust mitigation problem on a 2D, thin flat plate using onboard measurements. The objective is to solve the discrete-time optimal control problem of finding the pitch rate sequence that minimizes the lift perturbation, that is, the criterion where is the lift coefficient obtained by the unsteady vortex lattice method. The controller is modeled as an artificial neural network, and it is trained to minimize using deep reinforcement learning (DRL). To be optimal, we show that the controller must take as inputs the locations and circulations of the gust vortices, but these quantities are not directly observable from the onboard sensors. We therefore propose to use a Kalman particle filter (KPF) to estimate the gust vortices online from the onboard measurements. The reconstructed input is then used by the controller to calculate the appropriate pitch rate. We evaluate the performance of this method for gusts composed of one to five vortices. Our results show that (i) controllers deployed with full knowledge of the vortices are able to mitigate efficiently the lift disturbance induced by the gusts, (ii) the KPF performs well in reconstructing gusts composed of less than three vortices, but shows more contrasted results in the reconstruction of gusts composed of more vortices, and (iii) adding a KPF to the controller recovers a significant part of the performance loss due to the unobservable gust vortices.

Extreme weather events and severe flash floods during July 2021 caused numerous deaths and massive ecological disasters across Europe. The regionally overstrained environmental and socio-cultural resilience triggered an intensive discussion about cause and effect, responsibilities and public denouncement, and the financial consequences of climate-induced extreme events. In this article we analyze the flood event by four methodological approaches: (1) hermeneutics, with an analog interpretation of printed newspapers and sources; (2) text mining and natural language processing of digital newspaper articles available online; (3) precipitation and discharge models based on instrumental data; and (4) how the findings can be linked to the historical extreme floods of 1804 and 1910, based on documentary source analysis. These four approaches are used to compare and evaluate their consistency by tracking the course, consequences, and aftermaths of the flood disaster. The study shows a high consistency between the analog, digital, and instrumental data analysis. A combination of multidisciplinary methods and their application to historical events enables the evaluation of modern events. It enables to answer the question of return periods and intensities, which are indispensable for today’s risk assessments and their social contextualization, a desideratum in historical and modern climatology.

Purpose: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model. Methods: Twenty-three healthy subjects in three groups (nine adults (A), eight post-pubertal (PP) males and six pubertal (P) females) first sat or stood for six minutes and then performed a maximal graded test on a bicycle ergometer until exhaustion. We measured energy expenditure, oxygen uptake, ventilatory thresholds 1 and 2 and maximal oxygen uptake. The respiratory magnetometer plethysmography system measured four chest and abdomen distances using magnetometers sensors. We trained the models to predict energy expenditure based on the temporal convolutional networks model. Results: The respiratory magnetometer plethysmography system provided accurate energy expenditure estimation in groups A (R2 = 0.98), PP (R2 = 0.98) and P (R2 = 0.97). The temporal convolutional networks model efficiently estimates energy expenditure under sitting, standing and high levels of exercise intensities. Conclusion: Our results proved the respiratory magnetometer plethysmography system’s effectiveness in estimating energy expenditure for different age populations across various intensities of physical activity.

Deep reinforcement learning (DRL) is promising for solving control problems in fluid mechanics, but it is a new field with many open questions. Possibilities are numerous and guidelines are rare concerning the choice of algorithms or best formulations for a given problem. Besides, DRL algorithms learn a control policy by collecting samples from an environment, which may be very costly when used with Computational Fluid Dynamics (CFD) solvers. Algorithms must therefore minimize the number of samples required for learning (sample efficiency) and generate a usable policy from each training (reliability). This paper aims to (a) evaluate three existing algorithms (DDPG, TD3, and SAC) on a fluid mechanics problem with respect to reliability and sample efficiency across a range of training configurations, (b) establish a fluid mechanics benchmark of increasing data collection cost, and (c) provide practical guidelines and insights for the fluid dynamics practitioner. The benchmark consists in controlling an airfoil to reach a target. The problem is solved with either a low-cost low-order model or with a high-fidelity CFD approach. The study found that DDPG and TD3 have learning stability issues highly dependent on DRL hyperparameters and reward formulation, requiring therefore significant tuning. In contrast, SAC is shown to be both reliable and sample efficient across a wide range of parameter setups, making it well suited to solve fluid mechanics problems and set up new cases without tremendous effort. In particular, SAC is resistant to small replay buffers, which could be critical if full-flow fields were to be stored.