Miracle-dz/UniFAR-FTUs huggingface dataset:
Air Quality Forecast by Statistical Methods: Application to Portugal and Macao

Air pollution is a major concern issue for most countries in the world. In Portugal and Macao, the values of nitrogen dioxide (NO2), particulate matter (PM) and ozone (O3) are frequently above the concentration thresholds accepted as “good air quality. ” Portugal follows the European Union (EU) legislation (Directive 2008/50/EC) on air quality and Macao the air quality guidelines (AQG) from the WHO. Air quality forecasts are very important mitigation tools because of their ability to anticipate pollution events, and issue early warnings, allowing to take preventive measures and reduce impacts, by avoiding exposure. The work presented here refers to the statistical forecast of air pollutants for three regions: Greater Lisbon Area, Madeira Autonomous Region (both located in Portugal), and Macao Special Administrative Region (in Southern China). The presented statistical approach combines Classification and Regression Tree (CART) and multiple regression (MR) analysis to obtain optimized regression models. This consolidated methodology is now in operation for more than a decade in Portugal, and is subject to regular updates that reflect the ongoing research and the changes in the air quality monitoring network. Recently, the same methodology was applied to Macao in collaboration with the Macao Meteorological and Geophysical Bureau (SMG). Here, a statistical approach for air quality forecasting is described that has been proven to be successful, being able to forecast PM10, PM2.5, NO2, and O3 concentrations, for the next day, with a good performance. In general, all the models have shown a good agreement between the observed and forecasted concentrations (with R2 from 0.50 to 0.89), and were able to follow the concentration evolution trend. For some cases, there is a slight delay in the prediction trend. Moreover, the results obtained for pollution episodes have proven that statistical forecast can be an effective way of protecting public health.
Milk Transmission of HTLV-1 and the Need for Innovative Prevention Strategies

Breastfeeding is recommended by the World Health Organization for at least 6 months up to 2 years of age, and breast milk protects against several diseases and infections. Intriguingly, few viruses are transmitted via breastfeeding including Human T-cell leukemia virus Type 1 (HTLV-1). HTLV-1 is a highly oncogenic yet neglected retrovirus, which primarily infects CD4+ T-cells in vivo and causes incurable diseases like HTLV-1-associated inflammatory conditions or Adult T-cell leukemia/lymphoma (ATLL) after lifelong viral persistence. Worldwide, at least 5–10 million people are HTLV-1-infected and most of them are unaware of their infection posing the risk of silent transmissions. HTLV-1 is transmitted via cell-containing body fluids such as blood products, semen, and breast milk, which constitutes the major route of mother-to-child transmission (MTCT). Risk of transmission increases with the duration of breastfeeding, however, abstinence from breastfeeding as it is recommended in some endemic countries is not an option in resource-limited settings or underrepresented areas and populations. Despite significant progress in understanding details of HTLV-1 cell-to-cell transmission, it is still not fully understood, which cells in which organs get infected via the oral route, how these cells get infected, how breast milk affects this route of infection and how to inhibit oral transmission despite breastfeeding, which is an urgent need especially in underrepresented areas of the world. Here, we review these questions and provide an outlook how future research could help to uncover prevention strategies that might ultimately allow infants to benefit from breastfeeding while reducing the risk of HTLV-1 transmission.
The Cognitive Profile of Math Difficulties: A Meta-Analysis Based on Clinical Criteria

Math difficulties (MD) manifest across various domain-specific and domain-general abilities. However, the existing cognitive profile of MD is incomplete and thus not applicable in typical settings such as schools or clinics. So far, no review has applied inclusion criteria according to DSM or ICD, summarized domain-specific abilities or examined the validity of response time scores for MD identification. Based upon stringent clinical criteria, the current meta-analysis included 34 studies which compared cognitive performances of a group with MD (n = 680) and a group without MD (n = 1565). Criteria according to DSM and ICD were applied to identify MD (percentile rank ≤ 16, age range 8–12 years, no comorbidities/low IQ). Effect sizes for 22 abilities were estimated and separated by their level and type of scoring (AC = accuracy, RT = response time). A cognitive profile of MD was identified, characterized by distinct weaknesses in: (a) computation (calculation [AC], fact retrieval [AC]), (b) number sense (quantity processing [AC], quantity-number linking [RT], numerical relations [AC]), and (c) visual-spatial short-term storage [AC]. No particular strength was found. Severity of MD, group differences in reading performance and IQ did not significantly moderate the results. Further analyses revealed that (a) effects are larger when dealing with numbers or number words than with quantities, (b) MD is not accompanied by any weakness in abilities typically assigned to reading, and (c) weaknesses in visual-spatial short-term storage emphasize the notion that number and space are interlinked. The need for high-quality studies investigating domain-general abilities is discussed.
Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.
A Clinically Applicable Nomogram for Predicting the Risk of Invasive Mechanical Ventilation in Pneumocystis jirovecii Pneumonia

Objective Pneumocystis jirovecii pneumonia (PCP) is a life-threatening disease associated with a high mortality rate among immunocompromised patient populations. Invasive mechanical ventilation (IMV) is a crucial component of treatment for PCP patients with progressive hypoxemia. This study explored the risk factors for IMV and established a model for early predicting the risk of IMV among patients with PCP. Methods A multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram. Results In total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model’s performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110–6.159]; p = 0.028), oxygenation index (OR 0.217 [95% CI 0.078–0.604]; p = 0.003), and serum lactate dehydrogenase level (OR 1.864 [95% CI 1.040–3.341]; p = 0.037). Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 (95% CI 0.752–0.906) and 0.818 (95% CI 0.686–0.950) in predicting IMV in the training and validation cohorts, respectively, and had well-fitted calibration curves. Conclusions The nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes.
Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics

A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8–15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined via two manifold learning methods. We connect temporally consecutive brain states in the t distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain–computer interface (BCI) control, brain function research, and neural rehabilitation.
Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this spatio-temporal sampling constraint. Here, we evaluate a customized machine learning (ML) workflow to retrieve multi-temporal leaf-Chl levels, combining sub-centimeter resolution UAV-hyperspectral imagery (400–1,000 nm) with leaf-level reflectance spectra and SPAD measurements, capturing temporal correlations, selecting relevant predictors, and retrieving accurate results under different conditions. The study is performed within a phenotyping experiment to monitor wild tomato plants’ development. Several analyses were conducted to evaluate multiple ML strategies, including: (1) exploring sequential versus retraining learning; (2) comparing insights gained from using 272 spectral bands versus 60 pigment-based vegetation indices (VIs); and (3) assessing six regression methods (linear, partial-least-square regression; PLSR, decision trees, support vector, ensemble trees, and Gaussian process; GPR). Goodness-of-fit (R2) and accuracy metrics (MAE, RMSE) were determined using training/testing and validation data subsets to assess the models’ performance. Overall, while equally good performance was obtained using either PLSR, GPR, or random forest, results show: (1) the retraining strategy improved the ability of most of the approaches to model SPAD-based Chl dynamics; (2) comparative analysis between retrievals and validation data distributions informed the models’ ability to capture Chl dynamics through SPAD levels; (3) VI predictors slightly improved R2 (e.g., from 0.59 to 0.74 units for GPR) and accuracy (e.g., MAE and RMSE differences of up to 2 SPAD units) in specific algorithms; (4) feature importance examined through these methods, revealed strong overlaps between relevant bands and VI predictors, highlighting a few decisive spectral ranges and indices useful for retrieving leaf-Chl levels. The proposed ML framework allows the retrieval of high-quality spatially distributed and multi-temporal SPAD-based chlorophyll maps at an ultra-high pixel resolution (e.g., 7 mm).
Mechanism of Peptide Agonist Binding in CXCR4 Chemokine Receptor

Chemokine receptors are key G-protein-coupled receptors (GPCRs) that control cell migration in immune system responses, development of cardiovascular and central nervous systems, and numerous diseases. In particular, the CXCR4 chemokine receptor promotes metastasis, tumor growth and angiogenesis in cancers. CXCR4 is also used as one of the two co-receptors for T-tropic HIV-1 entry into host cells. Therefore, CXCR4 serves as an important therapeutic target for treating cancers and HIV infection. Apart from the CXCL12 endogenous peptide agonist, previous studies suggested that the first 17 amino acids of CXCL12 are sufficient to activate CXCR4. Two 17-residue peptides with positions 1–4 mutated to RSVM and ASLW functioned as super and partial agonists of CXCR4, respectively. However, the mechanism of peptide agonist binding in CXCR4 remains unclear. Here, we have investigated this mechanism through all-atom simulations using a novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method. The Pep-GaMD simulations have allowed us to explore representative binding conformations of each peptide and identify critical low-energy states of CXCR4 activated by the super versus partial peptide agonists. Our simulations have provided important mechanistic insights into peptide agonist binding in CXCR4, which are expected to facilitate rational design of new peptide modulators of CXCR4 and other chemokine receptors.
Off-Time Work-Related Smartphone Use and Bedtime Procrastination of Public Employees: A Cross-Cultural Study

While previous studies have examined the negative effects of work-related smartphone use after hours, little is known about whether and how it influences employees’ unhealthy sleep behavior (i.e., bedtime procrastination). Drawing on the ego depletion theory, this study explored the effects of work-related smartphone use after hours on bedtime procrastination. To further uncover potential cross-cultural differences, a sample of 210 public employees from the United States and 205 public employees from China were used. Results via path analysis revealed that off-time work-related smartphone use positively influenced bedtime procrastination via the mediating role of self-control depletion. These findings were consistent between the United States and Chinese sample; however, off-time work-related smartphone use after hours increased the likelihood of self-control depletion more strongly in the United States than in China. The implications of our findings for both theory and practice were discussed.
Development, Pathogenesis, and Regeneration of the Intervertebral Disc: Current and Future Insights Spanning Traditional to Omics Methods

The intervertebral disc (IVD) is the fibrocartilaginous joint located between each vertebral body that confers flexibility and weight bearing capabilities to the spine. The IVD plays an important role in absorbing shock and stress applied to the spine, which helps to protect not only the vertebral bones, but also the brain and the rest of the central nervous system. Degeneration of the IVD is correlated with back pain, which can be debilitating and severely affects quality of life. Indeed, back pain results in substantial socioeconomic losses and healthcare costs globally each year, with about 85% of the world population experiencing back pain at some point in their lifetimes. Currently, therapeutic strategies for treating IVD degeneration are limited, and as such, there is great interest in advancing treatments for back pain. Ideally, treatments for back pain would restore native structure and thereby function to the degenerated IVD. However, the complex developmental origin and tissue composition of the IVD along with the avascular nature of the mature disc makes regeneration of the IVD a uniquely challenging task. Investigators across the field of IVD research have been working to elucidate the mechanisms behind the formation of this multifaceted structure, which may identify new therapeutic targets and inform development of novel regenerative strategies. This review summarizes current knowledge base on IVD development, degeneration, and regenerative strategies taken from traditional genetic approaches and omics studies and discusses the future landscape of investigations in IVD research and advancement of clinical therapies.
Human Intestinal Organoids: Promise and Challenge

The study of human intestinal biology in healthy and diseased conditions has always been challenging. Primary obstacles have included limited tissue accessibility, inadequate in vitro maintenance and ethical constrains. The development of three-dimensional organoid cultures has transformed this entirely. Intestinal organoids are self-organized three-dimensional structures that partially recapitulate the identity, cell heterogeneity and cell behaviour of the original tissue in vitro. This includes the capacity of stem cells to self-renew, as well as to differentiate towards major intestinal lineages. Therefore, over the past decade, the use of human organoid cultures has been instrumental to model human intestinal development, homeostasis, disease, and regeneration. Intestinal organoids can be derived from pluripotent stem cells (PSC) or from adult somatic intestinal stem cells (ISC). Both types of organoid sources harbour their respective strengths and weaknesses. In this mini review, we describe the applications of human intestinal organoids, discussing the differences, advantages, and disadvantages of PSC-derived and ISC-derived organoids.
Tissue Resident Foxp3+ Regulatory T Cells: Sentinels and Saboteurs in Health and Disease

Foxp3+ regulatory T (Treg) cells are a CD4 T cell subset with unique immune regulatory function that are indispensable in immunity and tolerance. Their indisputable importance has been investigated in numerous disease settings and experimental models. Despite the extensive efforts in determining the cellular and molecular mechanisms operating their functions, our understanding their biology especially in vivo remains limited. There is emerging evidence that Treg cells resident in the non-lymphoid tissues play a central role in regulating tissue homeostasis, inflammation, and repair. Furthermore, tissue-specific properties of those Treg cells that allow them to express tissue specific functions have been explored. In this review, we will discuss the potential mechanisms and key cellular/molecular factors responsible for the homeostasis and functions of tissue resident Treg cells under steady-state and inflammatory conditions.
Cognitive Control Deficits in Children With Subthreshold Attention-Deficit/Hyperactivity Disorder

Subthreshold Attention-Deficit/Hyperactivity Disorder (ADHD) is defined as a neurobiological condition with some core inattentive or hyperactive/impulsive symptoms of ADHD which do not meet the full diagnosis clinically. Although it has been well documented that deficits in cognitive control, a high-level cognitive construct closely related to attention, are frequently found among children with ADHD, whether subthreshold ADHD is also associated with similar deficits remains unclear. In this study, we examined the attention functions and the cognitive control capacity (CCC) in children with ADHD (n = 39), those with subthreshold ADHD (n = 34), and typically developing peers (TD, n = 36). The results showed that the ADHD and subthreshold ADHD groups exhibited similar patterns of the impaired executive function of attention (revealed as an augment in flanker conflict effect) and reduced cognitive control capacity, and no significant difference was found between the two groups. These findings suggest that although children with subthreshold ADHD have not met the full criteria of ADHD, they showed reduced efficiency in cognitive control and attention function, similar to children with ADHD.
Enhanced fluid characterization in the millimeter-wave band using Gap Waveguide Technology

Microfluidic systems have emerged as a promising technology for molecular analysis, biodefence and microelectronics. Fluid sensing by microwave sensors based on the RF analysis offers new possibilities for the characterization of mediums by non-invasive methods. Several techniques are available in the frequency domain for analyzing the dielectric properties of liquids and their composition. Among them, one of the most accurate techniques is the resonant cavity methods. However, these techniques are incompatible with lab-on-chip process due its dimensions in the millimeter-wave band. In this context, a new structure called gap waveguide appears as a good candidate to overcome the principal drawbacks of the classical resonant cavities. This thesis presents the development of the gap waveguide technology in the millimeter-wave band. Other conventional technologies are discussed as well, to compare them with the performance in terms of gap waveguide losses. We also present the resonator design based on gap waveguide capable of working in the microfluidic sensing domain in the millimeter-wave band. In this context, we propose a comparative study between gap waveguide and Substrate Integrated Cavity (SIC) with the aim to characterize the fluid permittivity at 60 GHz. With this purpose, several prototypes have been manufactured with Printed Circuit Board (PCB) and Low Temperature Co-fired Ceramic (LTCC) technologies. An important experimental was carried out to validate work some critical steps in the LTCC process which are important for the gap waveguide manufacturing, particularly those related with the creation of cavities using LTCC materials.