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Renewable energy technologies are called to play a crucial role in the reduction of greenhouse gas emissions. Since most of these technologies did not yet reach grid parity, public policies have been implemented in order to foster their deployment. The approach that has been privileged in Europe is the demand-pull approach that aims at creating a demand for these new technologies and at stimulating their diffusion. This paper examines the effect of demand-pull policies on the diffusion of onshore wind power technology in six European countries: Denmark, France, Germany, Italy, Portugal and Spain. In a first step, a micro-founded model of diffusion is calibrated in order to replicate the observed diffusion of wind power in these six countries. In a second step, a counterfactual analysis is conducted by investigating several scenarios. By taking into account the complex self-sustained dynamics of diffusion and the learning spillovers that operate in the wind power sector, we can derive several insights about demand-pull policies. First, the impact of a demand-pull policy on the diffusion of wind power is determined by the stage at which it comes to support it. The effect seems to be stronger at the beginning of the diffusion. Second, international spillovers do operate in the wind power sector. These international spillovers however are not strong enough to foster the diffusion of wind power in a country having no demand-pull support. We can derive from these two statements that a strategy consisting in not implementing any demand-pull policy, with the expectation that international spillovers will reduce the cost of wind power and foster the diffusion of the technology that then shall become competitive, is not a good option for a country targeting a high share of wind power in its energy mix.

There is a lack of biomarkers to predict outcome following initial treatment in patients with high-grade ovarian cancer. We hypothesized that monitoring TP53 mutation (TP53m) in circulating tumor DNA (ctDNA) could be a tumor-specific biomarker. Patients enrolled in a prospective study (NCT03010124) consented to analysis of biological samples through the disease course. ctDNA was extracted and analyzed to detect the presence of TP53m. Next-generation sequencing was performed on tumor tissue to detect TP53m and on whole blood to detect clonal hematopoiesis of indeterminate potential (CHIP).A total of 102 samples were sequentially collected from 26 patients. ctDNA was detected in all patients at diagnosis. The same TP53m was found in ctDNA and tumor tissue in 77% of patients. TP53m in ctDNA was not CHIP related. During neoadjuvant chemotherapy, increasing ctDNA was associated with failure to achieve complete interval cytoreductive surgery in 60% of patients. Rising ctDNA or de novo TP53m seemed to be associated with a trend for worst survival compared with decrease or complete clearance: progression-free survival 10 versus 26.5 months, HR 3.2. Despite macroscopically complete surgery, 30% of patients had detectable ctDNA post-operatively and had worse survival than those with undetectable ctDNA. Monitoring TP53m in ctDNA during chemotherapy or after surgery could help guide the best adjuvant therapy.

Background: The identification of activating mutations in specific genes led to the development of targeted therapies for NSCLC. TKI directed against EGFR-mutations were the first to prove their major efficacy. Medical associations recommend their use as first and second-line metastatic treatments in EGFR-mutated patients. Our objective was to analyze the survival of EGFR-mutated patients treated beyond the second line of treatment. Methods: We performed a longitudinal, retrospective and analytical study at APHP (Assistance Publique Hopitaux de Paris) Saint Louis, Paris, France, from 1 January 2010 to 31 December 2020 (11 years), on EGFR-mutated patients with metastatic NSCLC which received TKI or chemotherapy (CT) in third-line. Results: Out of about 107 EGFR-mutated patients, 31 patients who benefited from TKI or CT in the third line of treatment were retained for this study. The mean age was 60.03 ± 11.93 years and the sex ratio male/female was 0.24. Mutations of exon 19, 21 and 20 were found in 21 (67.7%), 7 (22.6%) and 7 (22.6%) patients, respectively. Third-line treatment was CT for 16 patients (51.6%) and TKI for the 15 remaining patients (48.4%). Osimertinib was the most used TKI in third-line (n = 10/15; 66.67%). The median duration of third-line treatment was 5.37 months (range 0.53–37.6) and the median follow-up duration was 40.83 months (range 11.33–88.57). There was a significant difference in PFS between patients treated with TKI and CT in third-line (p = 0.028). For patients treated with CT in second-line, there was a significant difference of PFS (p < 0.001) and OS (p = 0.014) in favor of the use of TKI in third-line. Conclusions: For patients receiving CT in second-line, TKI appears to be a better alternative in third-line compared to CT. Osimertinib may be used in third line treatment if not used before.

Purpose Abiraterone acetate combined with prednisone improves survival in metastatic castration-resistant prostate cancer (mCRPC) patients. This oral anticancer agent may result in drug–drug interactions (DDI). We aimed to evaluate the prevalence of DDI with abiraterone and the possible determinants for the occurrence of these DDI. Methods We performed a single centre retrospective review from electronic medical records of mCRPC patients treated with abiraterone from 2011 to 2015. Potential DDI with abiraterone were identified using Micromedex and were categorized by a 4-point scale severity. Results Seventy-two out of ninety-five mCRPC pts (median age: 77 years [68–82]) had comorbidities. The median number of drugs used per patient was 7 [5–9]. 66 potential DDI with abiraterone were detected in 49 patients (52%): 39 and 61% were classified as major and moderate DDI, respectively. In the univariate analysis, pain ( p  < 0.0001), hypo-albuminemia ( p  = 0.032), and higher ECOG performance status (PS) ( p  = 0.013) were significantly associated with a higher risk of DDI with abiraterone. Pain ( p  < 0.0001) and PS ( p  = 0.018) remained significant in the multivariate analysis. Conclusions Polypharmacy is an issue among mCRPC patients. In our study, half of the patients have potential DDI with abiraterone. Patients with pain and poor PS are at higher risk of DDI with abiraterone. A medication review by a pharmacist is of crucial importance to prevent DDI with abiraterone.

The national reference network NETSARC+ provides remote access to specialized diagnosis and the Multidisciplinary Tumour Board (MTB) to improve the management and survival of sarcoma patients in France. The IGéAS research program aims to assess the potential of this innovative organization to address geographical inequalities in cancer management. Using the IGéAS cohort built from the nationwide NETSARC+ database, the individual, clinical, and geographical determinants of the 3-year overall survival of sarcoma patients in France were analyzed. The survival analysis was focused on patients diagnosed in 2013 (n = 2281) to ensure sufficient hindsight to collect patient follow-up. Our study included patients with bone (16.8%), soft-tissue (69%), and visceral (14.2%) sarcomas, with a median age of 61.8 years. The overall survival was not associated with geographical variables after adjustment for individual and clinical factors. The lower survival in precarious population districts [HR 1.23, 95% CI 1.02 to 1.48] in comparison to wealthy metropolitan areas (HR = 1) found in univariable analysis was due to the worst clinical presentation at diagnosis of patients. The place of residence had no impact on sarcoma patients’ survival, in the context of the national organization driven by the reference network. Following previous findings, this suggests the ability of this organization to go through geographical barriers usually impeding the optimal management of cancer patients.

Background: The identification of activating mutations in specific genes led to the development of targeted therapies for NSCLC. TKI directed against EGFR-mutations were the first to prove their major efficacy. Medical associations recommend their use as first and second-line metastatic treatments in EGFR-mutated patients. Our objective was to analyze the survival of EGFR-mutated patients treated beyond the second line of treatment. Methods: We performed a longitudinal, retrospective and analytical study at APHP (Assistance Publique Hopitaux de Paris) Saint Louis, Paris, France, from 1 January 2010 to 31 December 2020 (11 years), on EGFR-mutated patients with metastatic NSCLC which received TKI or chemotherapy (CT) in third-line. Results: Out of about 107 EGFR-mutated patients, 31 patients who benefited from TKI or CT in the third line of treatment were retained for this study. The mean age was 60.03 ± 11.93 years and the sex ratio male/female was 0.24. Mutations of exon 19, 21 and 20 were found in 21 (67.7%), 7 (22.6%) and 7 (22.6%) patients, respectively. Third-line treatment was CT for 16 patients (51.6%) and TKI for the 15 remaining patients (48.4%). Osimertinib was the most used TKI in third-line (n = 10/15; 66.67%). The median duration of third-line treatment was 5.37 months (range 0.53–37.6) and the median follow-up duration was 40.83 months (range 11.33–88.57). There was a significant difference in PFS between patients treated with TKI and CT in third-line (p = 0.028). For patients treated with CT in second-line, there was a significant difference of PFS (p < 0.001) and OS (p = 0.014) in favor of the use of TKI in third-line. Conclusions: For patients receiving CT in second-line, TKI appears to be a better alternative in third-line compared to CT. Osimertinib may be used in third line treatment if not used before.

Program synthesis methods aim to automatically generate programs restricted to a language that can explain a given specification of input-output pairs. While purely symbolic approaches suffer from a combinatorial search space, recent methods leverage neural networks to learn distributions over program structures to narrow this search space significantly, enabling more efficient search. However, for challenging problems, it remains difficult to train models to perform program synthesis in one shot, making test-time search essential. Most neural methods lack structured search mechanisms during inference, relying instead on stochastic sampling or gradient updates, which can be inefficient. In this work, we propose the Latent Program Network (LPN), a general algorithm for program induction that learns a distribution over latent programs in a continuous space, enabling efficient search and test-time adaptation. We explore how to train these networks to optimize for test-time computation and demonstrate the use of gradient-based search both during training and at test time. We evaluate LPN on ARC-AGI, a program synthesis benchmark that evaluates performance by generalizing programs to new inputs rather than explaining the underlying specification. We show that LPN can generalize beyond its training distribution and adapt to unseen tasks by utilizing test-time computation, outperforming algorithms without test-time adaptation mechanisms.

We estimate an index that measures the quality of the patented inventions related to Low Carbon Energy Technologies (LCETs) and delivered in seven countries during 1980-2010. This quality index is built using a Latent Factor Model (LFM) that synthesizes the information contained in patent documents. We capture a unique measure of patents quality, defined here as the economic value that is imputable to the technological advance resulting from the patented invention. A robust measure of the inventive performance of each country in the LCETs is obtained using the quality index. Several insights are derived from this measure about the technical advantages of countries and the dynamics of technologies' quality.

Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-gradient of current meta-gradient RL approaches. This bias comes from using the critic that is trained using the meta-learned discount factor for the advantage estimation in the outer objective which requires a different discount factor. Because the meta-learned discount factor is typically lower than the one used in the outer objective, the resulting bias can cause the meta-gradient to favor myopic policies. We propose a simple solution to this issue: we eliminate this bias by using an alternative, \\emph{outer} value function in the estimation of the outer loss. To obtain this outer value function we add a second head to the critic network and train it alongside the classic critic, using the outer loss discount factor. On an illustrative toy problem, we show that the bias can cause catastrophic failure of current meta-gradient RL approaches, and show that our proposed solution fixes it. We then apply our method to a more complex environment and demonstrate that fixing the meta-gradient bias can significantly improve performance.

Renewable energy technologies are called to play a crucial role in the reduction of greenhouse gas (GHG) emissions. Since most of these technologies did not yet reach grid parity, public policies have been implemented in order to foster their deployment. The approach that has been privileged in Europe is the demand-pull approach that aims at creating a demand for these new technologies and at stimulating their diffusion. This paper examines the effect of demand-pull policies on the diffusion of onshore wind power technology in six European countries: Denmark, France, Germany, Italy, Portugal and Spain. In a first step, a micro-founded model of diffusion is calibrated in order to replicate the observed diffusion of wind power in these six countries. In a second step, a counterfactual analysis is conducted by investigating several scenarios. By taking into account the complex self-sustained dynamics of diffusion and the learning spillovers that operate in the wind power sector, we can derive several insights about demand-pull policies. First, the impact of a demand-pull policy on the diffusion of wind power is determined by the stage at which it comes to support it. The effect seems to be stronger at the beginning of the diffusion. Second, international spillovers do operate in the wind power sector. These international spillovers however are not strong enough to foster the diffusion of wind power in a country having no demand-pull support. We can derive from these two statements that a strategy consisting in not implementing any demand-pull policy, with the expectation that international spillovers will reduce the cost of wind power and foster the diffusion of the technology that then shall become competitive, is not a good option for a country targeting a high share of wind power in its energy mix.