Miracle-dz/UniFAR-FTUs huggingface dataset:
The cost-effectiveness of tumor-treating fields therapy in patients with newly diagnosed glioblastoma.

BACKGROUND There is strong concern about the costs associated with adding tumor-treating fields (TTF) therapy to standard first-line treatment for glioblastoma (GBM). Hence, we aimed to determine the cost-effectiveness of TTF therapy for the treatment of newly diagnosed patients with GBM. METHODS We developed a 3-health-state Markov model. The perspective was that of the French Health Insurance, and the horizon was lifetime. We calculated the transition probabilities from the survival parameters reported in the EF-14 trial. The main outcome measure was incremental effectiveness expressed as life-years gained (LYG). Input costs were derived from the literature. We calculated the incremental cost-effectiveness ratio (ICER) expressed as cost/LYG. We used 1-way deterministic and probabilistic sensitivity analysis to evaluate the model uncertainty. RESULTS In the base-case analysis, adding TTF therapy to standard of care resulted in increases of life expectancy of 4.08 months (0.34 LYG) and €185 476 per patient. The ICER was €549 909/LYG. The discounted ICER was €596 411/LYG. Parameters with the most influence on ICER were the cost of TTF therapy, followed equally by overall survival and progression-free survival in both arms. The probabilistic sensitivity analysis showed a 95% confidence interval of the ICER of €447 017/LYG to €745 805/LYG with 0% chance to be cost-effective at a threshold of €100 000/LYG. CONCLUSION The ICER of TTF therapy at first-line treatment is far beyond conventional thresholds due to the prohibitive announced cost of the device. Strong price regulation by health authorities could make this technology more affordable and consequently accessible to patients.
Circadian Systems and Metabolism

Circadian systems direct many metabolic parameters and, at the same time, they appear to be exquisitely shielded from metabolic variations. Although the recent decade of circadian research has brought insights into how circadian periodicity may be generated at the molecular level, little is known about the relationship between this molecular feedback loop and metabolism both at the cellular and at the organismic level. In this theoretical paper, we conjecture about the interdependence between circadian rhythmicity and metabolism. A mathematical model based on the chemical reactions of photosynthesis demonstrates that metabolism as such may generate rhythmicity in the circadian range. Two additional models look at the possible function of feedback loops outside of the circadian oscillator. These feedback loops contribute to the robustness and sustainability of circadian oscillations and to compensation for long-and short-term metabolic variations. The specific circadian property of temperature compensation is put into the context of metabolism. As such, it represents a general compensatory mechanism that shields the clock from metabolic variations.
Retours sur la seconde explosion scolaire

D’une explosion scolaire a l’autre Les observateurs attentifs de l’ecole francaise n’ont pas tarde a le signaler (Prost, 1991 ; Rochex, 1991) : c’est bien a une seconde explosion scolaire que l’on assiste dans l’enseignement secondaire entre 1985 et 1995, comparable a celle qui a eu lieu dans les annees soixante. A l’epoque en effet, la frequentation du college s’est tres rapidement generalisee. Si un peu plus de la moitie seulement d’une classe d’âge entre en 6e en 1962, c’est le cas des 197.
DeSTM: Harnessing determinism in STMs for application development

Non-determinism has long been recognized as one of the key challenges which restrict parallel programmer productivity by complicating several phases of application development. While Software Transactional Memory (STM) systems have greatly improved the productivity of programmers developing parallel applications in a host of areas they still exhibit non-deterministic behavior leading to decreased productivity. While determinism in parallel applications which use traditional synchronization primitives (such as locks) has been relatively well studied, its interplay with STMs has not. In this paper we present DeSTM, a deterministic STM, which allows programmers to leverage determinism through the implementation, debugging and testing phases of application development. In this work we first adapt techniques which introduce determinism in applications which use traditional synchronization (such as locks) to work in conjunction with certain STMs. As one would expect, this does lead to performance degradation over a non-deterministic execution. Next we present, DeSTM, which uses novel techniques exploiting the properties of these STMs to dramatically improve the performance of deterministic executions. Further, DeSTM allows programmers to randomly change the deterministic schedule in a controlled fashion giving programmers access to a wide variety of execution schedules during application development. We demonstrate our approach on the STAMP benchmark suite. We first study the overheads that determinism introduces in STM applications and then demonstrate how DeSTM is able to improve performance of deterministic execution significantly, by over 50% in some applications and on average by about 35%. DeSTM also actually helped us detect, what we believe is a bug, in one of the benchmarks. Further, our approach is programmer friendly requiring no changes to application code.
Deregulation of mTORC 1 in ccRCC 1 Interplay between pVHL and mTORC 1 pathways in clear-cell renal cell carcinoma

COI: REDD1 monoclonal antibody reported herein is now being sold by Bethyl laboratories. Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Abstract mTORC1 is implicated in cell growth control and is extensively regulated. We previously reported that in response to hypoxia, mTORC1 is inhibited by REDD1. REDD1 is upregulated by HIF-1, and forced REDD1 expression is sufficient to inhibit mTORC1. REDD1-induced mTORC1 inhibition is TSC1/TSC2-dependent. In clear-cell renal cell carcinoma (ccRCC), VHL is frequently inactivated leading to constitutive activation of HIF-2 and/or HIF-1, which may be expected to upregulate REDD1 and inhibit mTORC1. However, mTORC1 is frequently activated in ccRCC and mTORC1 inhibitors are effective against this tumor type; a paradox herein examined. REDD1 was upregulated in VHL-deficient ccRCC by in silico microarray analyses, as well as qRT-PCR, western blot and immunohistochemistry. Vhl disruption in a mouse model was sufficient to induce Redd1. Using ccRCC-derived cell lines, we show that REDD1 upregulation in tumors is VHL-dependent, and that both HIF-1 and HIF-2 are, in a cell-type dependent manner, recruited to, and essential for, REDD1 induction. Interestingly, whereas mTORC1 is responsive to REDD1 in some tumors, strategies have evolved in others, such as mutations disrupting TSC1, to subvert mTORC1 inhibition by REDD1. Sequencing analyses of 77 ccRCCs for mutations in TSC1, TSC2 and REDD1, using PTEN as a reference, implicate the TSC1 gene, and possibly REDD1, as tumor suppressors in sporadic ccRCC. Understanding how ccRCCs become refractory to REDD1-induced mTORC1 inhibition should shed light into the molecular evolution of ccRCCs and may aid in patient selection for molecular targeted therapies. Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Beyond quality in early childhood education and care : postmodern perspectives

1. What this book is about 2. Theoretical perspectives: modernity and postmodernity, power and ethics 3. Constructing early childhood institution: what do we think it is? 4. Constructing the early childhood institution: what do we think they are for? 5. Beyond the discourse of quality to the discourse of meaning making 6. The Stockholm project: constructing a pedagogy that speaks in the voice of the child, the pedagogue and the parent 7. Pedagogical documentation: a practice for reflection and democracy 8. Minority directions in the majority world: threats and possibilities
Therapeutic temperature modulation in severe or moderate traumatic brain injury: a propensity score analysis of data from the Nationwide Japan Neurotrauma Data Bank.

OBJECTIVE In patients with severe traumatic brain injury (TBI), a randomized controlled trial revealed that outcomes did not significantly improve after therapeutic hypothermia (TH) or normothermia (TN). However, avoiding pyrexia, which is often associated with intracranial disorders, might improve clinical outcomes. The objective of this study was to compare neurological outcomes among patients with moderate and severe TBI after therapeutic temperature modulation (TTM) in the absence of other interventions. METHODS Data from 1091 patients were obtained from the Japan Neurotrauma Data Bank Project 2009, a cohort observational study. Patients with cardiac arrest, those with a Glasgow Coma Scale score of 3 and dilated fixed pupils, and those whose cause of death was injury to another area of the body were excluded, leaving 687 patients aged 16 years or older in this study. The patients were divided into 2 groups: the TTM group underwent TN (213 patients) or TH (82 patients), and the control group (392 patients) did not receive TTM. The primary end point for this study was the rate of poor outcome at hospital discharge, and the secondary end point was in-hospital death. Out of the 208 total items in the database, 29 variables that could potentially affect outcome were matched using the propensity score (PS) method in order to reduce selection bias and balance the baseline characteristics. RESULTS From each group, 141 patients were extracted using the PS-matching process. Among the patients in the TTM group, 29 had undergone TH and 112 had undergone TN. In a log-rank test using Kaplan-Meier survival curves, no significant differences in patient outcome or death were observed between the 2 groups (poor outcome, p = 0.83; death, p = 0.18). A Cox proportional-hazards regression analysis established the HR for poor outcome and mortality at 1.03 (95% CI 0.78-1.36, p = 0.83) and 1.34 (95% CI 0.87-2.07, p = 0.18), respectively. CONCLUSIONS There was no clear improvement in neurological outcomes after TTM in patients with moderate or severe TBI. To elucidate the role of TTM in patients with these injuries, a prospective study is needed with long-term follow-up using specific target temperatures.
A Curve Fitting Approach Using ANN for Converting CT Number to Linear Attenuation Coefficient for CT-based PET Attenuation Correction

Energy-mapping, the conversion of linear attenuation coefficients (μ) calculated at the effective computed tomography (CT) energy to those corresponding to 511 keV, is an important step in CT-based attenuation correction (CTAC) for positron emission tomography (PET) quantification. The aim of this study was to implement energy-mapping step by using curve fitting ability of artificial neural network (ANN). Eleven digital phantoms simulated by Geant4 application for tomographic emission (GATE) and 12 physical phantoms composed of various volume concentrations of iodine contrast were used in this study to generate energy-mapping curves by acquiring average CT values and linear attenuation coefficients at 511 keV of these phantoms. The curves were built with ANN toolbox in MATLAB. To evaluate the effectiveness of the proposed method, another two digital phantoms (liver and spine-bone) and three physical phantoms (volume concentrations of 3%, 10% and 20%) were used to compare the energy-mapping curves built by ANN and bilinear transformation, and a semi-quantitative analysis was proceeded by injecting 0.5 mCi FDG into a SD rat for micro-PET scanning. The results showed that the percentage relative difference (PRD) values of digital liver and spine-bone phantom are 5.46% and 1.28% based on ANN, and 19.21% and 1.87% based on bilinear transformation. For 3%, 10% and 20% physical phantoms, the PRD values of ANN curve are 0.91%, 0.70% and 3.70%, and the PRD values of bilinear transformation are 3.80%, 1.44% and 4.30%, respectively. Both digital and physical phantoms indicated that the ANN curve can achieve better performance than bilinear transformation. The semi-quantitative analysis of rat PET images showed that the ANN curve can reduce the inaccuracy caused by attenuation effect from 13.75% to 4.43% in brain tissue, and 23.26% to 9.41% in heart tissue. On the other hand, the inaccuracy remained 6.47% and 11.51% in brain and heart tissue when the bilinear transformation was used. Overall, it can be concluded that the bilinear transformation method resulted in considerable bias and the newly proposed calibration curve built by ANN could achieve better results with acceptable accuracy.
Análisis empírico del consumo, saliencia y credibilidad mediática como factores explicativos del posicionamiento actitudinal hacia la inmigración

Although today we know the importance of media in defining the reality of the immigration (Igartua et al. 2005; Cea D’Ancona, 2004), becomes highlighted the need for indicators capable of measuring media influence in the rise of racism and xenophobia. For this reason, the purpose of this research is aimed to advance in the design of survey indicators that make possible a better understanding of reproduction processes of attitudes toward immigration, and specifically those associated to media consumption. Thus, this study presents the results for a survey of 377 students of different degrees at the Universidad Complutense de Madrid. The questionnaire had two blocks designed with differentiated questions. First, an initial section referred to the consumption of media news. The second block was focused on the measurement of attitudes toward immigrants. The exploratory data analysis shows significant relationships between attitudes towards immigration and differential media consumption and the framing of immigration in news.
Optimized Hybrid Model Framework for Breast Cancer Classification

Breast diagnosis from pathology reports is a widely employed clinical method for diagnosing breast tumors. But it's challenging due to low contrast, high noise, and diverse appearances. Experienced professionals rely on factors like global context, local geometry, and intensity changes, acquired through years of clinical experience. To ease the burden on doctors, we propose integrating machine learning with diagnosis practice. However, effective and efficient breast tumor detection is crucial for automated disease diagnosis. In the last decade, various deep learning models have emerged for breast tumor detection. Traditional methods lack real-time responsiveness, accuracy, and scalability. To address these issues, we introduce an enhanced framework with hybrid model based on a multi-task cascaded convolutional network. This framework leverages a substantial dataset of clinically confirmed images with precise labels, employing a multi-task cascaded architecture. It involves two stages of deep convolutional networks designed to detect and recognize tumor affected area progressively, ensuring real-time operation and high accuracy. Additionally, we introduce an improved feature fusion-based augmentation method, enhancing diagnostic model performance. Our experiments demonstrate exceptional accuracy and time efficiency. Furthermore, the model's versatility is highlighted in the context of related diseases. Our proposed model showed a high accuracy of 97.69 for multi classification and 99.39 for binary classification. Also, our proposed framework when compared with many other hybrid model utilized less time (3.19 seconds per image) and resources making it efficient.
The syphilis care cascade: tracking the course of care after screening positive among men and transgender women who have sex with men in Lima, Peru

Objectives Syphilis is endemic among men who have sex with men (MSM) and transgender women in Latin America. The objective of this study was to assess if those who screen positive for syphilis are receiving appropriate care and treatment. Methods We use data from the 2011 Peruvian National HIV Sentinel Surveillance to describe the syphilis care cascade among high-risk MSM and transgender women. Medical records from participants who had a positive syphilis screening test at two of the enrolment sites in Lima were reviewed to determine their subsequent course of care. Results We identified a cohort of 314 syphilis seropositive participants (median age: 30, 33.7% self-identified as transgender). Only 284/314 (90.4%) participants saw a physician for evaluation within 28 days of their positive test. Of these, 72/284 (25.4%) were asked to return for confirmatory results before deciding whether or not to start treatment; however, 45/72 (62.5%) of these participants did not follow up within 28 days. Of the people prescribed three weekly doses of penicillin, 34/63 (54%) received all three doses on time. Conclusions Many MSM and transgender women with a positive syphilis screening test are lost at various steps along the syphilis care cascade and may have persistent infection. Interventions in this population are needed to increase testing, link seropositive patients into care and ensure that they receive appropriate and timely treatment.
Hot Water Load Prediction Based on a Combined Model of CNN-GRU and TCN

The imbalance between water supply and demand in the hot water systems of universities has become increasingly prominent, leading to long-term resource wastage. Traditional methods struggle to capture the dynamic changes in hot water usage, making it challenging to accurately predict hot water demand in universities. This poses significant importance for water management and design in educational institutions.Deep learning methods are well-suited for handling nonlinear load data and have shown significant effectiveness in load forecasting. In this paper, we propose a combined approach using Temporal Convolutional Networks (TCN) and Convolutional Neural Networks with Gated Recurrent Units (CNN-GRU) for predicting the hot water demand in university campuses. Collect historical hot water load data and combine it with relevant influencing factors such as daily maximum temperature, meteorological information, and corresponding holiday schedules to construct a rich set of input samples. We consider time features like periodicity and trends. Train the neural network model using several years of hot water load data from the campus and validate its predictive performance.The results indicate that this model can effectively capture the temporal variations in hot water usage and achieve high prediction accuracy. This research offers a new perspective on water demand forecasting for universities.
Approximation via cost-sharing: a simple approximation algorithm for the multicommodity rent-or-buy problem

We study the multicommodity rent-or-buy problem, a type of network design problem with economies of scale. In this problem, capacity on an edge can be rented, with cost incurred on a per-unit of capacity basis, or bought, which allows unlimited use after payment of a large fixed cost. Given a graph and a set of source-sink pairs, we seek a minimum-cost way of installing sufficient capacity on edges so that a prescribed amount of flow can be sent simultaneously from each source to the corresponding sink. The first constant-factor approximation algorithm for this problem was recently given by Kumar et al.; however, this algorithm and its analysis are both quite complicated, and its performance guarantee is extremely large. In this paper, we give a conceptually simple 12-approximation algorithm for this problem. Our analysis of this algorithm makes crucial use of cost sharing, the task of allocating the cost of an object to many users of the object in a "fair" manner. While techniques from approximation algorithms have recently yielded new progress on cost sharing problems, our work is the first to show the converse - those ideas from cost sharing can be fruitfully applied in the design and analysis of approximation algorithms.
A Gesture Segmentation Algorithm Based on Channel State Information Quotient Distance

This paper proposes a new measuring standard, CSI-quotient distance, for gesture segmentation. A new gesture signal segmentation method is proposed by combining CSI quotient distance with a sliding window algorithm. Firstly, we propose a new gesture segmentation standard by analyzing the physical characteristics of CSI quotient. Then, we propose a new gesture signal segmentation algorithm combining CSI quotient distance with the sliding window method. Finally, we implement the gesture segmentation system based on CSI quotient. Our experimental results show that our method can segment gestures at different speeds and achieve an average error of 1.2 cm in measuring moving distance.
A Comparative Analysis of Political Parties in Kenya, Zambia and the Democratic Republic of Congo

This article compares the characteristics and development of the main political parties of Kenya, Zambia and the Democratic Republic of Congo, starting from their independence in the 1960s up to the late 1990s. It offers an exploratory analysis of the differences and similarities that might have led to the, more or less, successful establishment of political parties and democratic regimes in these countries. The parties are compared along four dimensions: colonial heritage, the saliency of ethnicity, political communication between the party elite and the periphery, and their link to civil society. The study shows that the development of national parties at the time of independence was severely hindered by the colonizers. The inherited political structures encouraged the exploitation of both ethnic and regional links and further strengthened patronage networks. But also in more recent years, political leaders have used ethnicity as a strategic tool to strengthen their position in power. The comparative analysis shows that in those countries in which ethnicity was most salient, political parties were less democratic and less favourable for democratization. The analysis also highlights that the country with the least active political communication had the most difficult path towards a multiparty system. Finally, the article suggests that a civil society that is truly independent from the government, and incorporates powerful players in urban areas, is most likely to contribute to the development of a competitive party system.
Real-Time Emotion Recognition System for Dynamic Real-World Environments

Facial expressions are a direct and innate form of meaningful channel that conveys emotions non-verbally. Though considerable breakthroughs have been made in Automatic Factial Emotion Recognition (AFER), the research has been on lab-controlled datasets. Real-world scenarios pose expression-unrelated variations, such as illumination, occlusion, pose variations, skin-tone variation, subject identity bias, face deformation, motion blur, etc. There is a lack of data with such unconstrained conditions in contrast to the lab-controlled setting. With deep learning techniques, larger models bring significant accuracy but fail to work in real-time and are left unusable in real-world scenarios and even suffer from the explainability of the outputs. The research develops a lightweight Facial Emotion Recognition system for emotion recognition of facial images, which can operate in real-time and is suitable for use on devices with limited hardware resources. The developed FER system is comparable with previous research and SOTA models and also explainable with far fewer model parameters. With less than 600K model parameters, the proposed FER system achieves a weighted test accuracy of 60% on FER-2013 and 65% on FERPlus respectively, with an inference time of less than 560ms.