Miracle-dz/UniFAR-FTUs huggingface dataset:
Referral criteria for asthma: Consensus document.

Asthma is one of the most prevalent chronic diseases in Spain. Current treatments ensure that the disease is controlled in most cases. However, disease is often uncontrolled in daily clinical practice, mainly owing to underdiagnosis, loss to follow-up, and poor adherence to therapy. In order to improve this situation, we must coordinate all those health professionals who intervene in patient care. Therefore, the Spanish Society of Allergology and Clinical Immunology (SEAIC), the Spanish Society of Primary Care Physicians (SEMERGEN), the Spanish Society of Family and Community Medicine (semFYC), the Spanish Society of General and Family Physicians (SEMG), and the Spanish Society of Pneumology and Thoracic Surgery (SEPAR) have drawn up a consensus document in which they establish criteria for referral and guidelines for the diagnosis, control, and follow-up of patients with asthma. The document aims to facilitate continuing and improved care in this area.
Gender disparity in access to education, under-5 mortality, and economic growth in sub-Saharan Africa.

This paper empirically analyses the impact of gender disparity in access to education and under 5 mortality on economic growth in selected sub-Saharan African (SSA) countries from the period 2005 - 2020. The study engaged a panel data of 17 selected SSA countries sourced from the World Development Indicators (WDI) and the United Nations Institute for Statistics (UNIS) and applied the instrumental variable generalised method of moments analytical approach. The result shows that the interaction between gender parity in access to education and primary school enrolment has a significant impact on economic growth. The study concludes that policies to promote gender parity in access to education would be of paramount importance to increase economic growth towards the actualisation of sustainable development goal related to inclusive and equitable quality education and the promotion of lifelong learning opportunities (SDG4) in SSA countries.
Monoclonal antibody therapy demonstrates increased virulence of a lineage VII strain of Lassa virus in nonhuman primates

ABSTRACT Lassa virus (LASV) is a World Health Organization (WHO) priority pathogen that causes high morbidity and mortality. Recently, we showed that a combination of three broadly neutralizing human monoclonal antibodies known as Arevirumab-3 (8.9F, 12.1F, 37.2D) based on the lineage IV Josiah strain protected 100% of cynomolgus macaques against heterologous challenge with lineage II and III strains of LASV when therapy was initiated beginning at day 8 after challenge. LASV strains from Benin and Togo represent a new lineage VII that are more genetically diverse from lineage IV than strains from lineages II and III. Here, we tested the ability of Arevirumab-3 to protect macaques against a LASV lineage VII Togo isolate when treatment was administered beginning 8 days after exposure. Unexpectedly, only 40% of treated animals survived challenge. In a subsequent study we showed that Arevirumab-3 protected 100% of macaques from lethal challenge when treatment was initiated 7 days after LASV Togo exposure. Based on our transcriptomics data, successful Arevirumab-3 treatment correlated with diminished neutrophil signatures and the predicted development of T cell responses. As the in vitro antiviral activity of Arevirumab-3 against LASV Togo was equivalent to lineage II and III strains, the reduced protection in macaques against Togo likely reflects the faster disease course of LASV Togo in macaques than other strains. This data causes concern regarding the ability of heterologous vaccines and treatments to provide cross protection against lineage VII LASV isolates.
Application of network analysis process (ANP) in assessment of combating desertification alternatives

Selecting appropriate alternatives considering effective criteria are useful to decision making for arid areas managers. The present study tries to present the main desertification strategies based on quantitative method. Other methods of the assessment of desertification strategies do not study the dependency and the relationship between criteria and alternatives in decision-making level. These methods only rank criteria and alternatives with the hierarchical structure, from up to down, and specify their weights. But complex internal relations between criteria and alternatives and their effects to achieve the ultimate goal requires a network analyzer. So in this study network analysis process (ANP) model were used. For modeling and analysis the data, internal and external dependencies matrix between alternatives and criteria were calculated. Alternatives were weighted using the designed network model and the main alternatives were evaluated and prioritized. The results showed that among 16 criteria and 40 final alternative surveyed, the results showed that the alternative of vegetation cover development and reclamation (A23) with general rating (Di= 0.2249) is the most important alternative in combating desertification process in the study area, and alternatives of prevention of unsuitable land use changes (A18) and modification of ground water harvesting (A31) were in the next priority with general rating of 0.2245 and 0.1852, respectively. Therefore, it was suggested that the results and ranking obtained should be considered in controlling and reducing the effects of desertification and rehabilitating degraded lands plans.
MCTS on model-based Bayesian Reinforcement Learning for efficient learning in Partially Observable environments

Introduction Impressive advances in Reinforcement Learning on fully observable domains, thanks in part to Deep Learning techniques, have caused a growing interest in solving partially observable domains due to their success on ATARI games. These domains are typically modeled as Partially Observable Markov Decision Processes (POMDPs) [6], which are well-known to be hard to solve due to uncertainty as a result of stochastic transitions, partial observability, and unknown dynamics. While the work on Deep Learning using Recurrent Neural Nets (RNNs) on POMDPs is promising, their sample inefﬁciency and lack of addressing the exploration-exploitation trade-off encourages the search for complementing methods. Here we look at Bayesian model-based approaches, which promise the optimal solution to this fundamental issue of the Reinforcement Learning. The Bayesian RL (BRL) idea is to maintain a probability distribution over the possible dynamics of the POMDP and current state, and devise a action picking policy with respect to that distribution, explicitly reasoning over the uncertainty of the agent. In order to do so, BRL methods assume some parametrization of the dynamics and maintain a distribution over the unknown parameters. The iPOMDP [4, 5], for example, views the model as an inﬁnite Hidden Markov Model (iHMM) and speciﬁes the prior with a hierarchical Dirichlet Process (HDP). Other work models the posterior of the dynamics in continuous POMDPs as a Gaussian Process Dynamical Model (GPDM) [3], assuming Gaussian stochasticity. These, and others, provide methods for maintaining distributions over models over time in a principled manner. Few of these approaches, however, directly address the question of how to then select actions with respect to the posterior, instead relying on simple and expensive look-ahead searches consisting of full-Bellman updates of small horizons (
Comprehensive evaluation of body composition in a wide age range of Iranian adults using bioelectrical impedance analysis: Tehran Lipid and Glucose Study

Abstract Objective: To evaluate sex- and age-stratified body composition (BC) parameters in subjects with wide age range of 20–79 years. Design: Cross-sectional. Setting: Participants of Tehran Lipid and Glucose Study (TLGS). Participants: Two thousand nine hundred seventy participants met our inclusion criteria. They were divided into five age groups, and BC parameters were analysed based on sex and age using a bioelectrical impedance analyser (BIA). Result: The mean age of the participants was 42·1 ± 12·5 years, and 54 % of them were males. The mean BMI was 26·7 ± 3·7 kg/m2. Obesity indices were significantly higher in females (P < 0·001); however, skeletal muscle mass (SMM) and fat-free mass (FFM) were significantly higher in males (P < 0·001). Both SMM and FFM decreased significantly after the age of 50 years. Obesity indices significantly increased from the age group of 20–29 to 30–39 years in males and the age groups of 30–39 to 40–49 years and 40–49 to 50–59 years in females. The fat mass ratio (fat mass/SMM) showed two peaks in both sexes (after the ages of 30 and 50 years in males and 40 and 50 years in females). A strong correlation was found between BMI and percentage of body fat (r = 0·823 in females v. r = 0·768 in males). Conclusion: This is the first community-based study in the MENA region identifying sex- and age-stratified BC values using BIA. Our findings can be used as a reference for comparison in appropriate settings.
Hygroscopic Growth of Adsorbed Water Films on Smectite Clay Particles

Hygroscopic growth of adsorbed water films on clay particles underlies a number of environmental science questions, from the air quality and climate impacts of mineral dust aerosols to the hydrology and mechanics of unsaturated soils and sedimentary rocks. Here, we use molecular dynamics (MD) simulations to establish the relation between adsorbed water film thickness (h) and relative humidity (RH) or disjoining pressure (Π), which has long been uncertain due to factors including sensitivity to particle shape, surface roughness, and aqueous chemistry. We present a new MD simulation approach that enables precise quantification of Π in films up to six water monolayers thick. We find that the hygroscopicity of phyllosilicate mineral surfaces increases in the order mica < K-smectite < Na-smectite. The relationship between Π and h on clay surfaces follows a double exponential decay with e-folding lengths of 2.3 and 7.5 Å. The two decay length scales are attributed to hydration repulsion and osmotic phenomena in the electrical double layer (EDL) at the clay–water interface.
Political factors, household income, under five survival, and life expectancy in the Sahel region.

In recent decades, the Sahel region has experienced ongoing health crises, including high fertility rates, declining life expectancy, and low under-five survival rates. As such, this study investigates the impact of political factors and household income on under-five survival and life expectancy in the Sahel region, using secondary data from seven countries in the area. The data for the study was extracted from the World Development Indicators, and a panel Autoregressive Distributed Lag model was applied for analysis. The findings reveal that individuals in the Sahel region have a life expectancy of approximately 57 years, with an alarming rate of approximately 102 children per 1,000 births not surviving beyond age five. Political factors significantly contribute to reduction of under-five survival and life expectancy in the region, as does household income. Therefore, addressing government effectiveness, political stability, and household income is crucial to improving health outcomes in the Sahel countries, ultimately enhancing under-five survival rates and life expectancy across the region.
Relationship between placental location and fetal gender using ultrasonography: A prospective study from the Maternity and Children's Hospital, Najran, Saudi Arabia.

The study was a prospective cross-sectional design and was carried out at the Maternity and Children's Hospital in Najran, Saudi Arabia on 142 pregnant women . The objective of the study was to establish, utilising ultrasound scan , whether there is any link between the site of the placenta and the gender of the fetus. A higher percentage of female fetuses were identified. The majority of placentas (73, 51.4%) were found to be anterior, of which 66 (46.5%) and 7 (4.9%) were present in female and male fetuses, respectively. 58 (40.8%) placentas were noted to be posterior, i.e. females, 10 (7.0%); males, 48 (33.8%). 10 (7.0%) placentas were high, a position which was equally prevalent in both genders, i.e. females, 5 (3.5%); males 5 (3.5%), and 1 (0.7%), in a male fetus, was low-lying. A significant correlation between placental site and the gender of the fetus was revealed by the chi-squared test (p < 0.05) the sensitivity of ultrasound in predication fetal gendar was 95.1%. The outcome reveals a noteworthy association between the position of the placenta and the gender of the fetus.
Political factors, entrepreneurship and female employment vulnerability in sub-Saharan Africa.

This study examines the relationship between political factors, entrepreneurship, and female employment vulnerability in sub-Saharan Africa. Using data from the World Development Indicators (WDI) and World Governance Indicators (WGI) of the World Bank for the period 2001 - 2022, the study employs the Generalised Method of Moments to control for endogeneity. The results show that there exists an elastic relationship between voice and accountability and female vulnerability to employment in these sub-regions. It implies that a percentage increase in voice and accountability will result in a 11.9%, 3.07%, 1.08% decrease in female vulnerability to employment in Central, East, West and Southern Africa, respectively. These findings suggest that improving political institutions and reducing corruption could help to promote female entrepreneurship and reduce vulnerability in Sub-Saharan Africa.
Drug‐induced pneumonitis risk in diffuse large B‐cell/follicular lymphoma patients treated with R‐CHOP‐like regimen is associated with the use of granulocyte colony‐stimulating growth factors

Abstract Background Rituximab‐based combinations are the standard of care in diffuse large B‐cell lymphoma (DLBCL) and follicular lymphoma (FL). Despite being on market for over 20 years, some of the adverse effects associated with the use of rituximab are not well known. Drug‐induced interstitial pneumonitis (DIP) is a potentially fatal complication of the treatment. Granulocyte colony‐stimulating factors (G‐CSF) are supportive agents commonly used to prevent neutropenic infections. G‐CSF are reported to have pulmonary toxicity, but the risk of DIP is greater when used in combination with other potentially pulmotoxic agents. Methods In this retrospective study, we reported the G‐CSF use and risk of DIP in 234 DLBCL patients and 87 FL patients receiving R‐CHOP‐type immunochemotherapy. Results In 72% of patients, the treatment included a G‐CSF support. The overall incidence of treatment‐induced pneumonitis was 6.9% in this patient group. All the DIP cases (n = 16) were among patients receiving G‐CSF support (p = 0.03). Older age (over 60 years) and higher disease stage (Ann Arbor 3–4) also increased the risk of DIP. Conclusions These findings suggest that the use of G‐CSF increases the risk of DIP, when used in combination with rituximab‐containing regimen.
Elastic Multi-Gradient Descent for Parallel Continual Learning

The goal of Continual Learning (CL) is to continuously learn from new data streams and accomplish the corresponding tasks. Previously studied CL assumes that data are given in sequence nose-to-tail for different tasks, thus indeed belonging to Serial Continual Learning (SCL). This paper studies the novel paradigm of Parallel Continual Learning (PCL) in dynamic multi-task scenarios, where a diverse set of tasks is encountered at different time points. PCL presents challenges due to the training of an unspecified number of tasks with varying learning progress, leading to the difficulty of guaranteeing effective model updates for all encountered tasks. In our previous conference work, we focused on measuring and reducing the discrepancy among gradients in a multi-objective optimization problem, which, however, may still contain negative transfers in every model update. To address this issue, in the dynamic multi-objective optimization problem, we introduce task-specific elastic factors to adjust the descent direction towards the Pareto front. The proposed method, called Elastic Multi-Gradient Descent (EMGD), ensures that each update follows an appropriate Pareto descent direction, minimizing any negative impact on previously learned tasks. To balance the training between old and new tasks, we also propose a memory editing mechanism guided by the gradient computed using EMGD. This editing process updates the stored data points, reducing interference in the Pareto descent direction from previous tasks. Experiments on public datasets validate the effectiveness of our EMGD in the PCL setting.
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation

Recently, the advent of large language models (LLMs) has revolutionized generative agents. Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due to their ability to emotionally engage users. However, the absence of a comprehensive benchmark impedes progress in this field. To bridge this gap, we introduce CharacterEval, a Chinese benchmark for comprehensive RPCA assessment, complemented by a tailored high-quality dataset. The dataset comprises 1,785 multi-turn role-playing dialogues, encompassing 23,020 examples and featuring 77 characters derived from Chinese novels and scripts. It was carefully constructed, beginning with initial dialogue extraction via GPT-4, followed by rigorous human-led quality control, and enhanced with in-depth character profiles sourced from Baidu Baike. CharacterEval employs a multifaceted evaluation approach, encompassing thirteen targeted metrics on four dimensions. Comprehensive experiments on CharacterEval demonstrate that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. Source code, data source and reward model will be publicly accessible at https://github.com/morecry/CharacterEval.
HAAQI-Net: A non-intrusive neural music quality assessment model for hearing aids

This paper introduces HAAQI-Net, a non-intrusive deep learning model for music quality assessment tailored to hearing aid users. In contrast to traditional methods like the Hearing Aid Audio Quality Index (HAAQI), HAAQI-Net utilizes a Bidirectional Long Short-Term Memory (BLSTM) with attention. It takes an assessed music sample and a hearing loss pattern as input, generating a predicted HAAQI score. The model employs the pre-trained Bidirectional Encoder representation from Audio Transformers (BEATs) for acoustic feature extraction. Comparing predicted scores with ground truth, HAAQI-Net achieves a Longitudinal Concordance Correlation (LCC) of 0.9368, Spearman's Rank Correlation Coefficient (SRCC) of 0.9486, and Mean Squared Error (MSE) of 0.0064. Notably, this high performance comes with a substantial reduction in inference time: from 62.52 seconds (by HAAQI) to 2.54 seconds (by HAAQI-Net), serving as an efficient music quality assessment model for hearing aid users.
Free-form Shape Modeling in XR: A Systematic Review

Shape modeling research in Computer Graphics has been an active area for decades. The ability to create and edit complex 3D shapes has been of key importance in Computer-Aided Design, Animation, Architecture, and Entertainment. With the growing popularity of Virtual and Augmented Reality, new applications and tools have been developed for artistic content creation; real-time interactive shape modeling has become increasingly important for a continuum of virtual and augmented reality environments (eXtended Reality (XR)). Shape modeling in XR opens new possibilities for intuitive design and shape modeling in an accessible way. Artificial Intelligence (AI) approaches generating shape information from text prompts are set to change how artists create and edit 3D models. There has been a substantial body of research on interactive 3D shape modeling. However, there is no recent extensive review of the existing techniques and what AI shape generation means for shape modeling in interactive XR environments. In this state-of-the-art paper, we fill this research gap in the literature by surveying free-form shape modeling work in XR, with a focus on sculpting and 3D sketching, the most intuitive forms of free-form shape modeling. We classify and discuss these works across five dimensions: contribution of the articles, domain setting, interaction tool, auto-completion, and collaborative designing. The paper concludes by discussing the disconnect between interactive 3D sculpting and sketching and how this will likely evolve with the prevalence of AI shape-generation tools in the future.
Kodaira-Saito vanishing for the irregular Hodge filtration

After making correct, and then improving, our definition of the category of irregular mixed Hodge modules thanks to Mochizuki's recent results arXiv:2108.03843, we show how these results allow us to obtain Kodaira-Saito-type vanishing theorems for the irregular Hodge filtration of irregular mixed Hodge modules.
Hyperfine-enhanced gyroscope based on solid-state spins

Solid-state platforms based on electro-nuclear spin systems are attractive candidates for rotation sensing due to their excellent sensitivity, stability, and compact size, compatible with industrial applications. Conventional spin-based gyroscopes measure the accumulated phase of a nuclear spin superposition state to extract the rotation rate and thus suffer from spin dephasing. Here, we propose a gyroscope protocol based on a two-spin system that includes a spin intrinsically tied to the host material, while the other spin is isolated. The rotation rate is then extracted by measuring the relative rotation angle between the two spins starting from their population states, robust against spin dephasing. In particular, the relative rotation rate between the two spins can be enhanced by their hyperfine coupling by more than an order of magnitude, further boosting the achievable sensitivity. The ultimate sensitivity of the gyroscope is limited by the lifetime of the spin system and compatible with a broad dynamic range, even in the presence of magnetic noises or control errors due to initialization and qubit manipulations. Our result enables precise measurement of slow rotations and exploration of fundamental physics.
Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions

We consider an online two-stage stochastic optimization with long-term constraints over a finite horizon of $T$ periods. At each period, we take the first-stage action, observe a model parameter realization and then take the second-stage action from a feasible set that depends both on the first-stage decision and the model parameter. We aim to minimize the cumulative objective value while guaranteeing that the long-term average second-stage decision belongs to a set. We develop online algorithms for the online two-stage problem from adversarial learning algorithms. Also, the regret bound of our algorithm can be reduced to the regret bound of embedded adversarial learning algorithms. Based on this framework, we obtain new results under various settings. When the model parameters are drawn from unknown non-stationary distributions and we are given machine-learned predictions of the distributions, we develop a new algorithm from our framework with a regret $O(W_T+\sqrt{T})$, where $W_T$ measures the total inaccuracy of the machine-learned predictions. We then develop another algorithm that works when no machine-learned predictions are given and show the performances.