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Abstract Using integrability, we construct (to leading order in perturbation theory) the explicit form of twist-three light-ray operators in planar N $$ \mathcal{N} $$ = 4 SYM. This construction allows us to directly compute analytically continued CFT data at complex spin. We derive analytically the “magic” decoupling zeroes previously observed numerically. Using the Baxter equation, we also show that certain Regge trajectories merge together into a single unifying Riemann surface. Perhaps more surprisingly, we find that this unification of Regge trajectories is not unique. If we organize twist-three operators differently into what we call “cousin trajectories” we find infinitely more possible continuations. We speculate about which of these remarkable features of twist-three operators might generalize to other operators, other regimes and other theories.

Abstract Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, driving a quest for comprehensive characterization methods. We report a characterization of the ex vivo autofluorescence lifetime fingerprint of colorectal tissues obtained from 73 patients that underwent surgical resection. We specifically target the autofluorescence characteristics of collagens, reduced nicotine adenine (phosphate) dinucleotide (NAD(P)H), and flavins employing a fiber-based dual excitation (375 nm and 445 nm) optical imaging system. Autofluorescence-derived parameters obtained from normal tissues, adenomatous lesions, and adenocarcinomas were analyzed considering the underlying clinicopathological features. Our results indicate that differences between tissues are primarily driven by collagen and flavins autofluorescence parameters. We also report changes in the autofluorescence parameters associated with NAD(P)H that we tentatively attribute to intratumoral heterogeneity, potentially associated to the presence of distinct metabolic subpopulations. Changes in autofluorescence signatures of malignant tumors were also observed with lymphatic and venous invasion, differentiation grade, and microsatellite instability. Finally, we characterized the impact of radiative treatment in the autofluorescence fingerprints of rectal tissues and observed a generalized increase in the mean lifetime of radiated adenocarcinomas, which is suggestive of altered metabolism and structural remodeling. Overall, our preliminary findings indicate that multiparametric autofluorescence lifetime measurements have the potential to significantly enhance clinical decision-making in CRC, spanning from initial diagnosis to ongoing management. We believe that our results will provide a foundational framework for future investigations to further understand and combat CRC exploiting autofluorescence measurements.

Abstract SARS-CoV-2 caused the pandemic situation experienced since the beginning of 2020, and many countries faced the rapid spread and severe form of the disease. Mechanisms of interaction between the virus and the host were observed during acute phase, but few data are available when related to immunity dynamics in convalescents. We conducted a longitudinal study, with 51 healthy donors and 62 COVID-19 convalescent patients, which these had a 2-month follow-up after symptoms recovery. Venous blood sample was obtained from all participants to measure blood count, subpopulations of monocytes, lymphocytes, natural killer cells and dendritic cells. Serum was used to measure cytokines, chemokines, growth factors, anti-N IgG and anti-S IgG/IgM antibodies. Statistic was performed by Kruskal–Wallis test, and linear regression with days post symptoms and antibody titers. All analysis had confidence interval of 95%. Less than 35% of convalescents were anti-S IgM+, while more than 80% were IgG+ in D30. Anti-N IgG decreased along time, with loss of seroreactivity of 13%. Eosinophil count played a distinct role on both antibodies during all study, and the convalescence was orchestrated by higher neutrophil-to-lymphocyte ratio and IL-15, but initial stages were marked by increase in myeloid DCs, B1 lymphocytes, inflammatory and patrolling monocytes, G-CSF and IL-2. Later convalescence seemed to change to cytotoxicity mediated by T lymphocytes, plasmacytoid DCs, VEGF, IL-9 and CXCL10. Anti-S IgG antibodies showed the longest perseverance and may be a better option for diagnosis. The inflammatory pattern is yet present on initial stage of convalescence, but quickly shifts to a reparative dynamic. Meanwhile eosinophils seem to play a role on anti-N levels in convalescence, although may not be the major causative agent. We must highlight the importance of immunological markers on acute clinical outcomes, but their comprehension to potentialize adaptive system must be explored to improve immunizations and further preventive policies.

Current computer vision models require a significant amount of annotated data to improve their performance in a particular task. However, obtaining the required annotated data is challenging, especially in medicine. Hence, data augmentation techniques play a crucial role. In recent years, generative models have been used to create artificial medical images, which have shown promising results. This study aimed to use a state-of-the-art generative model, StyleGAN3, to generate realistic synthetic abdominal magnetic resonance images. These images will be evaluated using quantitative metrics and qualitative assessments by medical professionals. For this purpose, an abdominal MRI dataset acquired at Garcia da Horta Hospital in Almada, Portugal, was used. A subset containing only axial gadolinium-enhanced slices was used to train the model. The obtained Fréchet inception distance value (12.89) aligned with the state of the art, and a medical expert confirmed the significant realism and quality of the images. However, specific issues were identified in the generated images, such as texture variations, visual artefacts and anatomical inconsistencies. Despite these, this work demonstrated that StyleGAN3 is a viable solution to synthesise realistic medical imaging data, particularly in abdominal imaging.

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.

Abstract We describe solutions of asymptotically AdS3 Einstein gravity that are sourced by the insertion of operators in the boundary CFT2, whose dimension scales with the central charge of the theory. Previously, we found that the geometry corresponding to a black hole two-point function is simply related to an infinite covering of the Euclidean BTZ black hole [1]. However, here we find that the geometry sourced by the presence of a third black hole operator turns out to be a Euclidean wormhole with two asymptotic boundaries. We construct this new geometry as a quotient of empty AdS3 realized by domes and doors. The doors give access to the infinite covers that are needed to describe the insertion of the operators, while the domes describe the fundamental domains of the quotient on each cover. In particular, despite the standard fact that the Fefferman-Graham expansion is single-sided, the extended bulk geometry contains a wormhole that connects two asymptotic boundaries. We observe that the two-sided wormhole can be made single-sided by cutting off the wormhole and gluing on a “Lorentzian cap”. In this way, the geometry gives the holographic description of a three-point function, up to phases. By rewriting the metric in terms of a Liouville field, we compute the on-shell action and find that the result matches with the Heavy-Heavy-Heavy three-point function predicted by the modular bootstrap. Finally, we describe the geometric transition between doors and defects, that is, when one or more dual operators describe a conical defect insertion, rather than a black hole insertion.

Para alcançar a melhoria da qualidade dos processos produtivos e/ou do produto final, e obter processos com cada vez menos perdas e desperdícios, a metodologia do Seis Sigma utiliza um procedimento de melhoria continua conhecido como ciclo DMAIC. Este ciclo consiste em uma metodologia orientada à melhoria, otimização e estabilização de processos e projetos de negócios. Neste contexto, o objetivo deste estudo é aplicar as etapas Define, Measure e Analyze da metodologia DMAIC e analisar como ela pode contribuir para a redução de perdas de produto por vencimento no processo logístico em uma revenda de bebidas. Com isso, foi possível identificar os principais problemas das perdas de produtos por vencimento, entre elas a solicitação de produtos inovação em quantidade superior à demanda, os erros na marcação de puxada e a falta de engajamento do time comercial nas vendas dos produtos com idade crítica. Em seguida, esses problemas foram analisados para reconhecimento de suas causas raízes e posteriormente foram desenvolvidos planos de ação com o intuito de mitigar as causas raízes identificadas, contribuindo assim para a redução das perdas. Dentre as ações propostas, as primordiais são as voltadas para o time comercial, objetivando maior engajamento do time. Além disso, foi proposta a verificação da data de validade dos produtos de baixo giro e produtos de inovação antes da realização dos pedidos, bem como a criação de uma equipe focada em perdas.

Background: Success in resuscitation depends not only on the timeliness of the maneuvers but also on the quality of chest compressions. Factors such as the rescuer position and arm angle can significantly impact compression quality. Aim: This study explores the influence of rescuer positioning and arm angle on the quality of chest compressions among healthcare professionals experienced in cardiopulmonary resuscitation. Methods: In this international, multicentric, randomized crossover simulation trial with independent groups, healthcare professionals were assigned to one of four positions: kneeling on the floor, standing, standing on a step stool, and kneeling on the bed. Participants performed two 3-minute trials of uninterrupted chest compressions at arm angles of 90° and 105°. Compression quality was assessed, using manikin derived data. Results: A total of 76 participants entered the study. Those using a 90° arm angle exhibited higher compression scores than those at a 105° angle. Rescuers standing on a step stool maintained higher scores over time when compared to other groups. In contrast, rescuers kneeling on the bed consistently scored below 75% throughout the trial, with particularly low scores at the 105° angle. Conclusion: Rescuer position and arm angle significantly influence CPR quality, with a 90° arm angle and elevated positioning optimizing compression depth and effectiveness. The results recommend against kneeling on the bed due to its negative impact on chest compression quality.

Ao tratar de comunicação e saúde, esta pesquisa visa analisar as campanhas de prevenção ao suicídio e a recepção dessa comunicação pelos jovens universitários. A metodologia qualitativa envolve as técnicas de análise documental de campanhas de 23 organizações e entrevistas semiestruturadas sucessivas com 12 jovens, caracterizando-se como um estudo de recepção da publicidade. Os resultados evidenciam que as campanhas têm pouca repercussão entre os jovens estudados, e que sua comunicação, frequentemente, aborda a promoção da saúde mental em vez da prevenção ao suicídio, reforçando o tabu sobre o tema e descumprindo, como apontado pelo público nas práticas de recepção, a esperada função de informar sobre os meios de ajuda e atendimento ao suicídio iminente.

IntroductionThere remains a dearth of knowledge concerning the phenomenology of suicidal thoughts, with research focusing on reasons for feeling suicidal rather than their mental expression. While clinical interviews remain the standard phenomenological approach, such exploration of lived experiences may prove challenging for this sensitive topic. As a complementary alternative, the use of naturally-occurring online data is opportune for capturing elaborations on tabooed phenomena.MethodsIn this phenomenological study, we present a thematic analysis on lived experiences of suicidal thoughts as spontaneously reported by non-identified users of a Reddit online board (r/Depression), collecting 668 posts using the search terms “suicidal ideation,” “suicidal thoughts,” and “suicide.” Codes were grouped into descriptive categories summarizing the properties of thoughts, their effects, and their relation to suicide. Then, an interpretative synthesis yielded global themes connecting salient meanings on the experience of suicidal thoughts.ResultsWith a long-term and recurring nature, thoughts of suicide appear in the form of vivid imagery and daydreaming’s, initially bringing relief to adverse feelings but eventually becoming conditioned and all-consuming. Rather than a wonderment, they are experienced as intrusive thoughts by people struggling to make meaning of their occurrence. When conciliating the presence of unwanted thoughts, users express intricate relations to wishing or not to die, as well as varying perceptions of control over actions or fear of suicidal behavior.DiscussionWith an innovative application of big qualitative data into phenomenological analysis, this study contributes to an initial characterization of suicidal thoughts, uncovering findings that are not contemplated into current conceptualizations of suicidality. The analysis is limited by a restricted context of posts and unknown demographics, and further research with clinical interviews is warranted for in-depth exploration of suicidal thoughts.

On the road to the sixth generation of cellular networks (6G), the need to ensure a sustainable usage of natural resources, amid increased competition and cost pressures, has driven the adoption of text Self-Healing Mobile Networks to enhance operational efficiency of current and future wireless networks. This paradigm shift relies on Artificial Intelligence (AI) to increase automation of network functions, notably by applying predictive fault detection and automatic root-cause analysis. In this context, this paper proposes a Deep Learning (DL) model for text self-healing operations based on a Spatial Graph Convolutional Neural Network (SGCN), which is applied to evaluate the performance degradation of Base Stations (BSs) and uncover the underlying root-causes. The advantages of the proposed DL model are threefold. Firstly, it is especially suited for wireless network applications, leveraging the SGCN to account for spatial dependencies among BSs and their physical characteristics. Secondly, the proposed model offers the flexibility to process diverse types of predictive features, including Performance Management (PM), Fault Management (FM), or other data types. Thirdly, it incorporates an explainability module that pinpoints the input features, such as PM counters, with the most significant influence on BS performance, thereby shedding light on its root-cause factors. The proposed model was evaluated on a live 4G network dataset and the results confirmed its effectiveness in identifying BS performance degradation. An F1-score of 89.6% was achieved in the classification of performance failures, which includes a 27% reduction in false negatives compared to prior research outcomes. In a live network environment, this reduction translates into substantial improvements in Quality of Experience (QoE) for the end users and cost savings for the Mobile Network Operators (MNOs).

Abstract We initiate the study of holographic correlators for operators whose dimension scales with the central charge of the CFT. Differently from light correlators or probes, the insertion of any such maximally heavy operator changes the AdS metric, so that the correlator itself is dual to a backreacted geometry with marked points at the Poincaré boundary. We illustrate this new physics for two-point functions. Whereas the bulk description of light or probe operators involves Witten diagrams or extremal surfaces in an AdS background, the maximally heavy two-point functions are described by nontrivial new geometries which we refer to as “spacetime bananas”. As a universal example, we discuss the two-point function of maximally heavy scalar operators described by the Schwarzschild black hole in the bulk and we show that its onshell action reproduces the expected CFT result. This computation is nonstandard, and adding boundary terms to the action on the stretched horizon is crucial. Then, we verify the conformal Ward Identity from the holographic stress tensor and discuss important aspects of the Fefferman-Graham patch. Finally we study a Heavy-Heavy-Light-Light correlator by using geodesics propagating in the banana background. Our main motivation here is to set up the formalism to explore possible universal results for three- and higher-point functions of maximally heavy operators.

Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device’s placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas.

Abstract BackgroundThe use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians. ObjectiveThis cross-sectional study aims to assess the feasibility of this technology when used by parents—the real intended end users. MethodsPhysicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 (“totally disagree”) to 5 (“totally agree”). ResultsMost recordings had quality (physicians’ recordings: 253/297, 85.2%; parents’ recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5). ConclusionsOur results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.

Mounting evidence progressively appreciates the vital interplay between immunity and metabolism in a wide array of immunometabolic chronic disorders, both autoimmune and non-autoimmune mediated. The immune system regulates the functioning of cellular metabolism within organs like the brain, pancreas and/or adipose tissue by sensing and adapting to fluctuations in the microenvironment’s nutrients, thereby reshaping metabolic pathways that greatly impact a pro- or anti-inflammatory immunophenotype. While it is agreed that the immune system relies on an adequate nutritional status to function properly, we are only just starting to understand how the supply of single or combined nutrients, all of them termed immunonutrients, can steer immune cells towards a less inflamed, tolerogenic immunophenotype. Polyphenols, a class of secondary metabolites abundant in Mediterranean foods, are pharmacologically active natural products with outstanding immunomodulatory actions. Upon binding to a range of receptors highly expressed in immune cells (e.g. AhR, RAR, RLR), they act in immunometabolic pathways through a mitochondria-centered multi-modal approach. First, polyphenols activate nutrient sensing via stress-response pathways, essential for immune responses. Second, they regulate mammalian target of rapamycin (mTOR)/AMP-activated protein kinase (AMPK) balance in immune cells and are well-tolerated caloric restriction mimetics. Third, polyphenols interfere with the assembly of NLR family pyrin domain containing 3 (NLRP3) in endoplasmic reticulum-mitochondria contact sites, inhibiting its activation while improving mitochondrial biogenesis and autophagosome-lysosome fusion. Finally, polyphenols impact chromatin remodeling and coordinates both epigenetic and metabolic reprogramming. This work moves beyond the well-documented antioxidant properties of polyphenols, offering new insights into the multifaceted nature of these compounds. It proposes a mechanistical appraisal on the regulatory pathways through which polyphenols modulate the immune response, thereby alleviating chronic low-grade inflammation. Furthermore, it draws parallels between pharmacological interventions and polyphenol-based immunonutrition in their modes of immunomodulation across a wide spectrum of socioeconomically impactful immunometabolic diseases such as Multiple Sclerosis, Diabetes (type 1 and 2) or even Alzheimer’s disease. Lastly, it discusses the existing challenges that thwart the translation of polyphenols-based immunonutritional interventions into long-term clinical studies. Overcoming these limitations will undoubtedly pave the way for improving precision nutrition protocols and provide personalized guidance on tailored polyphenol-based immunonutrition plans.

This study aimed to investigate and compare the performance, perceptual and reaction skills and neuromuscular control indicators of sub-elite (SEG) and elite (EG) karate athletes during the execution of a Gyaku Tsuki punch. The study included 14 male athletes, equally divided into two subgroups according to their current competitive level. We analyzed the peak and mean linear velocity of the wrist, linear peak acceleration/deceleration of the wrist, braking time, pre-motor time, motor time, reaction time, movement time and co-contraction index between selected muscle groups. EG athletes presented higher values in almost all performance variables, with the exception of the mean linear velocity of the wrist, which was similar between the groups. In the perceptual and reaction skills, the EG athletes presented shorter time durations with the exception of the pre-motor time, which did not reveal significant differences. The only significant difference in the indicators of neuromuscular control were found during the deceleration phase, where the EG athletes presented a higher co-contraction index between the biceps brachii and the triceps brachii. In conclusion, the EG athletes, in addition to being faster to react, faster to accelerate the wrist, could perform the braking in less time than the SEG athletes, making the technique less perceptible to the opponent.

This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to facilitate navigation, optimize routes, and improve system performance through Edge/Fog integration. The methodology includes the use of tetrachromatic LED-equipped luminaires with On–Off Keying (OOK) modulation and a mesh cellular hybrid structure. Detailed airport modeling and user analysis (pedestrians and luggage/passenger carriers) equipped with PINPIN optical sensors are conducted. A VLC-specific communication protocol with coding and decoding techniques ensures reliable data transmission, while wayfinding algorithms offer real-time guidance. The results show effective data transmission and localization, enabling self-localization, travel direction inference, and route optimization. Agent-based simulations demonstrate improved traffic control, with analyses of user halting and average speed. This approach provides reliable indoor navigation independent of GPS signals, enhancing accessibility and convenience for airport users. The integration of VLC with Edge/Fog architecture ensures efficient movement through complex airport layouts.

Abstract We use the S-matrix bootstrap to carve out the space of unitary, analytic, crossing symmetric and supersymmetric graviton scattering amplitudes in nine, ten and eleven dimensions. We extend and improve the numerical methods of our previous work in ten dimensions. A key new tool employed here is unitarity in the celestial sphere. In all dimensions, we find that the minimal allowed value of the Wilson coefficient α, controlling the leading correction to maximal supergravity, is very close but not equal to the minimal value realized in Superstring theory or M-theory. This small difference may be related to inelastic effects that are not well described by our numerical extremal amplitudes. Although α has a unique value in M-theory, we found no evidence of an upper bound on α in 11D.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

Nowadays, great efforts of ongoing research are devoted to hybrid-electric propulsion technology that offers various benefits, such as reduced noise and pollution emissions and enhanced aircraft performance and fuel efficiency. The ability to estimate the performance of an aircraft in any flight situation in which it may operate is essential for aircraft development. In the current study, a simulation model was developed that allows estimating the flight performance and analyzing the mission of a fixed-wing multi-rotor Unmanned Aerial Vehicle (UAV) with a hybrid electric propulsion system (HEPS), with both conventional and Vertical Takeoff and Landing (VTOL) capabilities. The control is based on the continuous specification of pitch angle, propulsion thrust, and lift thrust to achieve the required conditions of a given flight segment. Six different missions were considered to analyze the effect of control parameters exhibiting the most influence on the UAV mission performance. An appropriate set of control parameters was selected through a multidimensional parametric study. The results show that the control parameters, if not well tuned, affect the mission performance: for example, in the deceleration transition, a longer time to reduce the cruise speed to stand still may be the result because the controller struggles to adjust the pitch angle. In addition, the implemented methodology captures the effects of transient maneuvers, unlike typical quasi-static analysis without the complexity of full simulation models.

Back pain is regularly associated with biomechanical changes in the spine. The traditional methods to assess spine biomechanics use ionising radiation. Vertebral Metrics (VM) is a non-invasive instrument developed by the authors in previous research that assesses the spinous processes’ position. However, the spine model used by VM is not accurate. To overcome it, the present paper proposes a pioneering and simple articulated model of the spine built through the data collected by VM. The model is based on the spring–mass system and uses the Levenberg–Marquardt algorithm to find the arrangement of vertebral bodies. It represents the spine as rigid geometric transformations from one vertebra to the other when the extremity vertebrae are stationary. The validation process used the Bland–Altman method to compare the Cobb and the intervertebral angles computed by the model with the radiographic exams of eight patients diagnosed with Ankylosing Spondylitis. The results suggest that the model is valid; however, previous clinical information would improve outcomes by customising the lower and upper vertebrae positions, since the study revealed that the C6 rotation slightly influences the computed angles. Applying VM with the new model could make a difference in preventing, monitoring, and early diagnosing spinal disorders.

Abstract Recent scholarly investigation of suicidal ideation has been largely based on identifying associated factors and using ideation-to-action theories to explain its occurrence. However, this approach may not be sufficient, as many aspects of suicidal ideation fall beyond the reach of such conceptualizations. The overemphasis on explaining rather than understanding this phenomenon is a significant factor in this insufficiency. As such, it is argued that qualitative methods that use data to derive theories could offer a more nuanced understanding of suicidal ideation. By adopting bottom-up approaches, researchers can explore how individuals experience and understand suicidal ideation and how it relates to their lives and experiences. Furthermore, use of qualitative research methods could aid in development of more accurate and inclusive definitions that are more firmly grounded in data.

The current paradigm in Mobile Wireless Networks (MWNs) operation is being defied by the increasing importance of Machine Learning (ML) and Artificial Intelligence (AI). Nevertheless, another paradigm shift is rising with recent developments in causal inference and causal discovery, which, although having the potential to be applied to MWNs, have been relatively unexplored. This paper aims to develop a data-driven methodology using unsupervised ML and Conditional Independence Tests (CITs), typically used in causal discovery tasks, to identify distinct network performance patterns and pinpoint causal factors to explain them. The proposed methodology was first evaluated with crowdsourcing data from User Equipments (UEs). Afterwards, a dataset from a Long-Term Evolution (LTE) network, composed of a set of arbitrary performance indicators and configuration parameters, was considered. The crowdsourcing dataset, containing multiple network speed tests, revealed that the measured uplink throughput contributed the most to the observed performance patterns due to the used Radio Access Technologies (RATs). Furthermore, the LTE dataset revealed a causal relationship between the number of reserved signalling resources in the Physical Uplink Control Channel (PUCCH) and the UE uplink throughput. Notwithstanding, the key contribution of this paper is the consideration of causal-based concepts and methods for network operations enhancement.

Abstract Objective To evaluate the efficacy of the hormonal and nonhormonal approaches to symptoms of sexual dysfunction and vaginal atrophy in postmenopausal women. Data Sources We conducted a search on the PubMed, Embase, Scopus, Web of Science, SciELO, the Cochrane Central Register of Controlled Trials (CENTRAL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, as well as on clinical trial databases. We analyzed studies published between 1996 and May 30, 2020. No language restrictions were applied. Selection of Studies We selected randomized clinical trials that evaluated the treatment of sexual dysfunction in postmenopausal women. Data Collection Three authors (ACAS, APFC, and JL) reviewed each article based on its title and abstract. Relevant data were subsequently taken from the full-text article. Any discrepancies during the review were resolved by consensus between all the listed authors. Data Synthesis A total of 55 studies were included in the systematic review. The approaches tested to treat sexual dysfunction were as follows: lubricants and moisturizers (18 studies); phytoestrogens (14 studies); dehydroepiandrosterone (DHEA; 8 studies); ospemifene (5 studies); vaginal testosterone (4 studies); pelvic floor muscle exercises (2 studies); oxytocin (2 studies); vaginal CO2 laser (2 studies); lidocaine (1 study); and vitamin E vaginal suppository (1 study). Conclusion We identified literature that lacks coherence in terms of the proposed treatments and selected outcome measures. Despite the great diversity in treatment modalities and outcome measures, the present systematic review can shed light on potential targets for the treatment, which is deemed necessary for sexual dysfunction, assuming that most randomized trials were evaluated with a low risk of bias according to the Cochrane Collaboration risk of bias tool. The present review is registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42018100488).

Abstract We reduce the computation of three point function of three spinning operators with arbitrary polarizations in 𝒩 = 4 SYM to a statistical mechanics problem via the hexagon formalism. The central building block of these correlation functions is the hexagon partition function. We explore its analytic structure and use it to generate perturbative data for spinning three point functions. For certain polarizations and any coupling, we express the full asymptotic three point function in determinant form. With the integrability approach established we open the ground to study the large spin limit where dualities with null Wilson loops and integrable pentagons must appear.

In the last years there has been a huge interest in the study of the human microbiome, and the genital tract one has been no exception. However, we still fail to fully understand the normal and abnormal genital tract microbiome. Despite the increasing knowledge, the diagnosis of vaginitis is mostly empirical and many interventions, such as the use of probiotics, are not supported by good quality evidence.

Introdução: Os chatbots são sistemas de processamento de linguagem natural que atuam como um agente de conversação, imitando as interações humanas. Recentemente, o interesse pelos chatbots aumentou e eles foram usados em vários sectores. A saúde não foi exceção, dado o crescente interesse por este tipo de tecnologia aliado à necessidade de apoiar os doentes em casa (Adamopoulou et al., 2020). Usando métodos baseados em regras ou inteligência artificial (IA), os chatbots de saúde podem ajudar a melhorar e automatizar os serviços no setor de saúde (Nadarzynski et al., 2019), (Swick, 2021). Objetivo: Este trabalho apresenta o GECA, um chatbot de cuidados preventivos, que disponibiliza informação, aconselhamento e acompanhamento de doentes que se encontram em tratamento domiciliário. Desta forma, é possível uma vigilância constante da recuperação e estado de saúde do paciente, apoiando o paciente em testes remotos, adesão à medicação e estado de saúde. Metodologia: O GECA foi implementado usando as linguagens de programação Python e Kotlin. O utilizador interage com o GECA tirando dúvidas ou fornecendo informações ao chatbot. Diferentes técnicas de IA são usadas não só para entender o que foi dito pelo utilizador mas também para perceber a sua intenção. Considerando esta informação, conjuntamente com o conhecimento já adquirido e o estado de saúde do utilizador, o GECA envia a resposta mais adequada. Resultados: O GECA ajuda pacientes COVID positivos e com demência a monitorizar o seu estado de saúde em casa, fornecendo instruções sobre a medicação e o estado de saúde, além de verificar os sintomas atuais, alertando o utilizador sobre a necessidade de contactar o médico caso seja detetado algum comportamento anormal. Para além desta gestão de saúde domiciliária, o GECA disponibiliza também ao utilizador as informações típicas de saúde e jogos cognitivos que podem auxiliar no combate a doenças cognitivas, o que é particularmente benéfico para indivíduos com demência. Todas estas interações com o GECA podem ser feitas por texto e/ou voz, em português ou inglês. Conclusões: GECA é um chatbot desenvolvido especificamente para atendimento preventivo. Ele oferece informação, aconselhamento e acompanhamento a doentes em tratamento domiciliário, proporcionando uma solução económica e personalizada.

Blueberries, red fruits enriched in polyphenols and fibers, are envisaged as a promising nutraceutical intervention in a plethora of metabolic diseases. Prediabetes, an intermediate state between normal glucose tolerance and type 2 diabetes, fuels the development of complications, including hepatic steatosis. In previous work, we have demonstrated that blueberry juice (BJ) supplementation benefits glycemic control and lipid profile, which was accompanied by an amelioration of hepatic mitochondrial bioenergetics. The purpose of this study is to clarify the impact of long-term BJ nutraceutical intervention on cellular mechanisms that govern hepatic lipid homeostasis, namely autophagy and endoplasmic reticulum (ER) stress, in a rat model of prediabetes. Two groups of male Wistar rats, 8-weeks old, were fed a prediabetes-inducing high-fat diet (HFD) and one group was fed a control diet (CD). From the timepoint where the prediabetic phenotype was achieved (week 16) until the end of the study (week 24), one of the HFD-fed groups was daily orally supplemented with 25 g/kg body weight (BW) of BJ (HFD + BJ). BW, caloric intake, glucose tolerance and insulin sensitivity were monitored throughout the study. The serum and hepatic lipid contents were quantified. Liver and interscapular brown and epidydimal white adipose tissue depots (iBAT and eWAT) were collected for histological analysis and to assess thermogenesis, ER stress and autophagy markers. The gut microbiota composition and the short-chain fatty acids (SCFAs) content were determined in colon fecal samples. BJ supplementation positively impacted glycemic control but was unable to prevent obesity and adiposity. BJ-treated animals presented a reduction in fecal SCFAs, increased markers of arrested iBAT thermogenesis and energy expenditure, together with an aggravation of HFD-induced lipotoxicity and hepatic steatosis, which were accompanied by the inhibition of autophagy and ER stress responses in the liver. In conclusion, despite the improvement of glucose tolerance, BJ supplementation promoted a major impact on lipid management mechanisms at liver and AT levels in prediabetic animals, which might affect disease course.

Biological therapies have transformed high-burden treatments. As the patent and exclusivity period for biological medicines draws to a close, there is a possibility for the development and authorization of biosimilars. These products boast comparable levels of safety, quality, and effectiveness to their precursor reference products. Biosimilars, although similar to reference products, are not identical copies and should not be considered generic substitutes for the original. Their development and evaluation involve a rigorous step-by-step process that includes analytical, functional, and nonclinical evaluations and clinical trials. Clinical studies conducted for biosimilars aim to establish similar efficacy, safety, and immunogenicity, rather than demonstrating a clinical benefit, as with the reference product. However, although the current knowledge regarding biosimilars has significantly increased, several controversies and misconceptions still exist regarding their immunogenicity, extrapolation, interchangeability, substitution, and nomenclature. The development of biosimilars stimulates market competition, contributes toward healthcare sustainability, and allows for greater patient access. However, maximizing the benefits of biosimilars requires cooperation between regulators and developers to ensure that patients can benefit quickly from access to these new therapeutic alternatives while maintaining high standards of quality, safety, and efficacy. Recognizing the inherent complexities of comprehending biosimilars fully, it is essential to focus on realistic approaches, such as fostering open communication between healthcare providers and patients, encouraging informed decision-making, and minimizing risks. This review addresses the regulatory and manufacturing requirements for biosimilars and provides clinicians with relevant insights for informed prescribing.

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.

Abstract Some quantities in quantum field theory are dominated by so-called leading logs and can be re-summed to all loop orders. In this work we introduce a notion of stampede which is a simple time-evolution of a bunch of particles which start their life in a corner — on the very right say — and hop their way to the opposite corner — on the left — through the repeated action of a quantum Hamiltonian. Such stampedes govern leading logs quantities in certain quantum field theories. The leading euclidean OPE limit of correlation functions in the fishnet theory and null double-scaling limits of correlators in N $$ \mathcal{N} $$ = 4 SYM are notable examples. As an application, we use these results to extend the beautiful bootstrap program of Coronado [1] to all octagons functions with arbitrary diagonal bridge length.

Abstract We work out the map between null polygonal hexagonal Wilson loops and spinning three point functions in large N conformal gauge theories by mapping the variables describing the two different physical quantities and by working out the precise normalization factors entering this duality. By fixing all the kinematics we open the ground for a precise study of the dynamics underlying these dualities — most notably through integrability in the case of planar maximally supersymmetric Yang-Mills theory.

Abstract Background Previous studies have demonstrated the feasibility of using an asthma app to support medication management and adherence but failed to compare with other measures currently used in clinical practice. However, in a clinical setting, any additional adherence measurement must be evaluated in the context of both the patient and physician perspectives so that it can also help improve the process of shared decision making. Thus, we aimed to compare different measures of adherence to asthma control inhalers in clinical practice, namely through an app, patient self‐report and physician assessment. Methods This study is a secondary analysis of three prospective multicentre observational studies with patients (≥13 years old) with persistent asthma recruited from 61 primary and secondary care centres in Portugal. Patients were invited to use the InspirerMundi app and register their inhaled medication. Adherence was measured by the app as the number of doses taken divided by the number of doses scheduled each day and two time points were considered for analysis: 1‐week and 1‐month. At baseline, patients and physicians independently assessed adherence to asthma control inhalers during the previous week using a Visual Analogue Scale (VAS 0–100). Results A total of 193 patients (72% female; median [P25–P75] age 28 [19–41] years old) were included in the analysis. Adherence measured by the app was lower (1 week: 31 [0–71]%; 1 month: 18 [0–48]%) than patient self‐report (80 [60–95]) and physician assessment (82 [51–94]) (p 0.05). There was a moderate correlation between patient self‐report and physician assessment (ρ = 0.596, p

ABSTRACT Brazilian frog farming has a history of production that began in the state of Rio de Janeiro in 1935. Over the years, this activity has spread throughout Brazil, with technological advances that have improved the productivity and health of enterprises. However, structuring the production chain has presented moments of growth and decline, culminating in low production compared with other aquaculture activities. Despite efforts focused on restructuring the chain, data on enterprises and their actors remain scarce. To obtain information on the current scenario of frog farming in the state of Rio de Janeiro, in this study, an online survey of data on frog producers in Rio de Janeiro was conducted. In general, frog farming in Rio de Janeiro has not shown substantial improvements in terms of structuring. Its dynamics are still linked to lowproduction, family run enterprises, little insertion into the market, and not being formalized, encompassing aspects related to fiscal, environmental, and health issues. Therefore, the restructuring process of the frog chain in the state depends on overcoming the bottlenecks mentioned in this study. These predominantly refer to the availability of information on the relevant aspects of management and regularization of production.

Summary: Background: Validated questionnaires are used to assess asthma control over the past 1–4 weeks from reporting. However, they do not adequately capture asthma control in patients with fluctuating symptoms. Using the Mobile Airways Sentinel Network for airway diseases (MASK-air) app, we developed and validated an electronic daily asthma control score (e-DASTHMA). Methods: We used MASK-air data (freely available to users in 27 countries) to develop and assess different daily control scores for asthma. Data-driven control scores were developed based on asthma symptoms reported by a visual analogue scale (VAS) and self-reported asthma medication use. We included the daily monitoring data from all MASK-air users aged 16–90 years (or older than 13 years to 90 years in countries with a lower age of digital consent) who had used the app in at least 3 different calendar months and had reported at least 1 day of asthma medication use. For each score, we assessed construct validity, test–retest reliability, responsiveness, and accuracy. We used VASs on dyspnoea and work disturbance, EQ-5D-VAS, Control of Allergic Rhinitis and Asthma Test (CARAT), CARAT asthma, and Work Productivity and Activity Impairment: Allergy Specific (WPAI:AS) questionnaires as comparators. We performed an internal validation using MASK-air data from Jan 1 to Oct 12, 2022, and an external validation using a cohort of patients with physician-diagnosed asthma (the INSPIRERS cohort) who had had their diagnosis and control (Global Initiative for Asthma [GINA] classification) of asthma ascertained by a physician. Findings: We studied 135 635 days of MASK-air data from 1662 users from May 21, 2015, to Dec 31, 2021. The scores were strongly correlated with VAS dyspnoea (Spearman correlation coefficient range 0·68–0·82) and moderately correlated with work comparators and quality-of-life-related comparators (for WPAI:AS work, we observed Spearman correlation coefficients of 0·59–0·68). They also displayed high test–retest reliability (intraclass correlation coefficients range 0·79–0·95) and moderate-to-high responsiveness (correlation coefficient range 0·69–0·79; effect size measures range 0·57–0·99 in the comparison with VAS dyspnoea). The best-performing score displayed a strong correlation with the effect of asthma on work and school activities in the INSPIRERS cohort (Spearman correlation coefficients 0·70; 95% CI 0·61–0·78) and good accuracy for the identification of patients with uncontrolled or partly controlled asthma according to GINA (area under the receiver operating curve 0·73; 95% CI 0·68–0·78). Interpretation: e-DASTHMA is a good tool for the daily assessment of asthma control. This tool can be used as an endpoint in clinical trials as well as in clinical practice to assess fluctuations in asthma control and guide treatment optimisation. Funding: None.

Chatbots have become increasingly popular in the healthcare industry. In the area of preventive care, chatbots can provide personalized and timely solutions that aid individuals in maintaining their well-being and forestalling the development of chronic conditions. This paper presents GECA, a chatbot designed specifically for preventive care, that offers information, advice, and monitoring to patients who are undergoing home treatment, providing a cost-effective, personalized, and engaging solution. Moreover, its adaptable architecture enables extension to other diseases and conditions seamlessly. The chatbot’s bilingual capabilities enhance accessibility for a wider range of users, including those with reading or writing difficulties, thereby improving the overall user experience. GECA’s ability to connect with external resources offers a higher degree of personalization, which is a crucial aspect in engaging users effectively. The integration of standards and security protocols in these connections allows patient privacy, security and smooth adaptation to emerging healthcare information sources. GECA has demonstrated a remarkable level of accuracy and precision in its interactions with the diverse features, boasting an impressive 97% success rate in delivering accurate responses. Presently, preparations are underway for a pilot project at a Portuguese hospital that will conduct exhaustive testing and evaluate GECA, encompassing aspects such as its effectiveness, efficiency, quality, goal achievability, and user satisfaction.

The performance of any Mobile Wireless Network (MWN) is dependent on the appropriate level of radio coverage, with Path Loss (PL) models being a valuable resource for its evaluation. Recently, advancements in Machine Learning (ML) and Deep Neural Networks (DNNs) have been applied to radio propagation to produce new data-driven PL models. Notoriously, these advancements have also allowed the inclusion of non-classical inputs, such as satellite images. However, data-driven PL models are often developed under the assumption that training and test data distributions are similar, which is a weak assumption in real-world scenarios. Thus, generalization (i.e., the model’s ability to perform on different data distributions) is a crucial aspect of data-driven PL models in the context of Mobile Network Operators (MNOs). This paper proposes a new data-driven PL model, the Ubiquitous Satellite Aided Radio Propagation (USARP) model, developed to enhance the geographical generalization capabilities of empirical PL models, by using satellite images. The USARP model considers self-supervised learning to extract general data representations of the radio environment from satellite images, improving the PL prediction Root Mean Square Error (RMSE) of the $3^{rd}$ Generation Partnership Project (3GPP) PL model in the order of 9 dB, and for a data distribution distinct from the training data. Moreover, it was demonstrated the potential of the USARP model in terms of geographical and radio environment generalization. Although the generalization capabilities of ML regression algorithms are limited, the chosen USARP architecture and the use of regularization techniques had a positive impact on its geographical generalization performance.

The use of devices that produce stochastic whole-body vibration as a resource for rehabilitation and training programs has been founded on the theory of stochastic resonance. However, the prescription of rehabilitation and training programs must be preceded by the verification of imposed-vibration magnitude and of how it can be affected by the presence of an individual on the devices. The aim of this research was to characterize and analyze the effect of an individual's mass on the vibratory stimulus provided by stochastic whole-body vibration (SWBV) devices. The sample consisted of 30 repetitions for each one of the 6 vibration levels of the SWBV device (level 02, 04, 06, 08, 10 and 12), performed in two experimental situations (Without Load; Load [70Kg]; ≈ 35 kg on the right and left surfaces of the platform). For the antero-posterior, latero-lateral, and vertical directions, all variables showed significant differences between treatments, levels and interaction between experimental factors (p

BackgroundCytolytic vaginosis (CV) is a little-known, controversial condition that is typically not considered for women presenting with vulvovaginitis symptoms. Objective: The objective of this scoping review was to identify and compile the global evidence on CV.MethodsA medical librarian searched Prospero, Wiley Cochrane Library, Ovid Embase, Ovid Medline, EBSCO CINAHL, ProQuest Dissertations and Theses Global, and Scopus, from inception to April 4, 2019 and updated to October 17, 2021. Studies were eligible if they discussed CV. Two independent reviewers conducted study selection and data extraction.ResultsSixty-four studies were identified, with 67% of studies (n = 43) published since 2007. Studies were from around the world, including the United States (28%, n = 18), Brazil (11%, n = 7), Portugal (11%, n = 7), and China (11%, n = 7). Fifty percent of studies (n = 32) were reviews; the remainder were observational; and of these, 78% (n = 25) were cross-sectional. The most frequent topics included: diagnosis (19%, n = 12), prevalence (17%, n = 11), and overview of CV (50%, n = 32). Evidence for prevalence in symptomatic women (median prevalence of 5%, interquartile range 3%-8%) was based only on 16% of studies (n = 10) with minimal evidence on prevalence in asymptomatic women and across different geographic regions. Microbiological findings, including abundant lactobacilli and fragmented epithelial cells, were found useful to distinguish between CV and vulvovaginal candidiasis, and Lactobacillus crispatus was noted to dominate the vaginal flora in women with CV. Most studies used subjective criteria to diagnose CV as the condition lacks gold-standard microscopic criteria. The suggested primary treatment (baking soda irrigations) was largely based on expert opinion, and there was minimal evidence on associations between CV and other conditions.ConclusionKnowledge gaps currently exist in all realms of CV research. Additional research is needed to confirm the validity of CV and ensure that women are diagnosed and treated effectively.

To investigate the effect of furosemide on Staphylococcus aureus α-hemolysin (α-HL) channel in planar lipid bilayers by electrophysiological characterization and molecular docking studies. Planar lipid bilayer membranes were prepared and α-HL (0.07 mg/mL) was added to the standard solution in cis compartment of the experimental chamber. All experiments were performed at room temperature using an Axopatch 200A amplifier in the voltage clamp mode. At pH 7.5, α-HL channels were usually in a high conductance ~4 nS and rarely switch to low conductance states. After the ion channel was incorporated in bilayer membrane, the furosemide was also added to the standard solution to the cis compartment. To docking studies, atomics coordinates for the α-HL heptameric channel was retrieved from PDB ID (7AHL) and the structure of furosemide was removed from the PubChem, their coordinates were built and minimized with Avogadro software. The molecular docking experiments were performed using the Dockthor online. The furosemide inhibited (P

Abstract Background A 73-year-old male patient who had a history of Human Immunodeficiency Virus (HIV) infection for over 20 years was diagnosed with SARS-CoV-2 infection. Case presentation The patient was admitted to the Intensive Care Unit (ICU), where he remained for 25 days, due to a severe condition. Intubation, hemodialysis, and tracheostomy were necessary to maintain homeostasis. In addition to regular treatment with etravirine, dolutegravir, darunavir, and ritonavir for highly active antiretroviral therapy, the patient received tocilizumab, which showed a great recovery in the patient’s condition. Conclusion The patient had several risk factors, such as male gender, age > 70 years, and arterial hypertension. The use of tocilizumab was of great importance in the patient’s recovery since the drug increased his immune response, which is deficient, due to HIV infection.

Immunoglobulin G (IgG) antibodies play an important role in the immune response against viruses such as SARS-CoV-2. As the effector functions of IgG are modulated by N-glycosylation of the Fc region, the structure and possible function of the IgG N-glycome has been under investigation in relation to divergent COVID-19 disease courses. Through LC-MS analysis we studied both total IgG1 and spike protein-specific IgG1 Fc glycosylation of 129 German and 163 Brazilian COVID-19 patients representing diverse patient populations. We found that hospitalized COVID-19 patients displayed decreased levels of total IgG1 bisection and galactosylation and lowered anti-S IgG1 fucosylation and bisection as compared to mild outpatients. Anti-S IgG1 glycosylation was dynamic over the disease course and both anti-S and total IgG1 glycosylation were correlated to inflammatory markers. Further research is needed to dissect the possible role of altered IgG glycosylation profiles in (dys)regulating the immune response in COVID-19.

Abstract We use the numerical S-matrix bootstrap method to obtain bounds on the two leading Wilson coefficients (or low energy constants) of the chiral lagrangian controlling the low-energy dynamics of massless pions. This provides a proof of concept that the numerical S-matrix bootstrap can be used to derive non-perturbative bounds on massless EFTs in more than two spacetime dimensions.

The inclusion of students in socioeconomic vulnerability in the university demands the implementation of student assistance programs. The objective of this research is to evaluate the student assistance programs of the University of Brasilia (UnB) through the criteria of importance, quality and knowledge. The evaluated programs are: food aid, student housing, socioeconomic and emergency aid, permanence scholarship, access to foreign language and book tickets. For this, 189 questionnaires answered by the benefited students were analyzed through cluster analysis. Programs that offer direct financial resources received suggestions for updating their value and criticism of the constant late payment; other programs showed difficulties in achieving their objectives.

The inflammatory pathway driven by TNF-α, through its receptors TNFR1 and TNFR2, is a common feature in the pathogenesis of chronic kidney disease (CKD), regardless of the initial disease cause. Evidence correlates the chronic inflammatory status with decreased renal function. Our aim was to evaluate the potential of TNF receptors as biomarkers for CKD diagnosis and staging, as well as their association with the progression of renal lesions, in rat models of early and moderate CKD. We analyzed the circulating levels of inflammatory molecules—tumor necrosis factor-alpha (TNF-α), tumor necrosis factor receptor 1 (TNFR1) and 2 (TNFR2) and tissue inhibitor of metalloproteinase-1 (TIMP-1)—and studied their associations with TNFR1 and TNFR2 renal expression, glomerular and tubulointerstitial lesions, and with biomarkers of renal (dys)function. An increase in all inflammatory markers was observed in moderate CKD, as compared to controls, but only circulating levels of both TNFR1 and TNFR2 were significantly increased in the early disease; TNFR2 serum levels were negatively correlated with eGFR. However, only TNFR2 renal expression increased with CKD severity and showed correlations with the score of mild and advanced tubular lesions. Our findings suggest that renal TNFR2 plays a role in CKD development, and has potential to be used as a biomarker for the early detection and progression of the disease. Still, the potential value of this biomarker in disease progression warrants further investigation.

Abstract Background Recent evidence suggests that generation of neutrophil extracellular traps (NETosis), one of the components of immunothrombosis, is associated with the pathogenesis of both venous thromboembolism and sickle cell disease (SCD). NETosis is a complex process regulated by several proteins such as peptidyl arginine deaminase 4 (PADI4), neutrophil elastase (ELANE), and myeloperoxidase (MPO). Among these regulators, PADI4 is responsible of histone citrullination, an essential step for NETosis. Accordingly, its inhibition has been recently cited as a promising therapeutic strategy for diseases such as SCD. Although attractive, this strategy requires supportive evidence of its role in the pathogenesis of SCD. Patients and Methods Patients from two independent cohorts were enrolled in this study. Samples were obtained at steady state (53 patients) or during acute episodes of vaso‐occlusive crisis (VOC; 28 patients) in patients from cohort 1. mRNA was extracted from granulocytes to analyze PADI4, ELANE, and MPO expression by qPCR. Furthermore, plasma activity of PADI4 was assessed from an independent cohort in 15 patients, within 24 hours from admission for VOC. Race‐matched healthy individuals from the same geographic regions were used as controls for each cohort. Results and Conclusions Higher levels of gene expression of PADI4 and ELANE were observed during VOC. Furthermore, plasma activity of PADI4 was higher in acute VOC when compared to healthy individuals. These results demonstrate that NETosis regulators are modulated during acute VOC, and pave the way for studies of PADI4 inhibition as a therapeutic strategy for acute VOC in SCD.

The ability to estimate radio coverage accurately is fundamental for planning and optimizing any wireless network, notably when a new generation, as the 5th Generation (5G), is in an early deployment phase. The knowledge acquired from radio planning of previous generations must be revisited, particularly the used path loss and antennas models, as the 5G propagation is intrinsically distinct. This paper analyses a new beamforming antenna model and distinct path loss models - 3rd Generation Partnership Project (3GPP) and Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications (mmMAGIC) - applying them to evaluate 5G coverage in 3-Dimensional (3D) synthetic and real scenarios, for outdoor and indoor environments. Further, real 5G Drive Tests (DTs) were used to evaluate the 3GPP path loss model accuracy in Urban Macro (UMa) scenarios. For the new antenna model, it is shown that the use of beamforming with multiple vertical beams is advantageous when the Base Station (BS) is placed below the surrounding buildings; in regular UMa surroundings, one vertical beam provides adequate indoor coverage and a maximized outdoor coverage after antenna tilt optimization. The 3GPP path loss model exhibited a Mean Absolute Error (MAE) of 21.05 dB for Line-of-Sight (LoS) and 14.48 dB for Non-Line-of-Sight (NLoS), compared with real measurements. After calibration, the MAE for LoS and NLoS decreased to 5.45 dB and 7.51 dB, respectively. Moreover, the non-calibrated 3GPP path loss model led to overestimations of the 5G coverage and user throughput up to 25% and 163%, respectively, when compared to the calibrated model predictions. The use of Machine Learning (ML) algorithms resulted in path loss MAEs within the range of 4.58 dB to 5.38 dB, for LoS, and within the range of 3.70 dB to 5.96 dB, for NLoS, with the Random Forest (RF) algorithm attaining the lowest error.

The ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user’s environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user’s side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.