Wind Shapes the Growth Strategies of Trees in a Tropical Forest.

In tropical forests, trees strategically balance growth patterns to optimise fitness amid multiple environmental stressors. Wind poses the primary risk to a tree's mechanical stability, prompting developments such as thicker trunks to withstand the bending forces. Therefore, a trade-off in resource allocation exists between diameter growth and vertical growth to compete for light. We explore this trade-off by measuring the relative wind mortality risk for 95 trees in a tropical forest in Panama and testing how it varies with tree size, species and wind exposure. Surprisingly, local wind exposure and tree size had minimal impact on wind mortality risk; instead, species wood density emerged as the crucial factor. Low wood density species exhibited a significantly greater wind mortality risk, suggesting a prioritisation of competition for light over biomechanical stability. Our study highlights the pivotal role of wind safety in shaping the life-history strategy of trees and structuring diverse tropical forests.

Design and implementation of a metagenomic analytical pipeline for respiratory pathogen detection.

OBJECTIVE: We developed an in-house bioinformatics pipeline to improve the detection of respiratory pathogens in metagenomic sequencing data. This pipeline addresses the need for short-time analysis, high accuracy, scalability, and reproducibility in a high-performance computing environment. RESULTS: We evaluated our pipeline using ninety synthetic metagenomes designed to simulate nasopharyngeal swab samples. The pipeline successfully identified 177 out of 204 respiratory pathogens present in the compositions, with an average processing time of approximately 4 min per sample (processing 1 million paired-end reads of 150 base pairs). For the estimation of all the 470 taxa included in the compositions, the pipeline demonstrated high accuracy, identifying 420 and achieving a correlation of 0.9 between their actual and predicted relative abundances. Among the identified taxa, 27 were significantly underestimated or overestimated, including only three clinically relevant pathogens. We also validated the pipeline by applying it to a clinical dataset from a study on metagenomic pathogen characterization in patients with acute respiratory infections and successfully identified all pathogens responsible for the diagnosed infections. These findings underscore the pipeline's effectiveness in pathogen detection and highlight its potential utility in respiratory pathogen surveillance.

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

Study of Pentacyclic Triterpenes from Lyophilised Aguaje: Anti-Inflammatory and Antioxidant Properties.

Mauritia flexuosa (M. flexuosa), commonly known as Aguaje or Moriche palm, is traditionally recognised in South America for its medicinal properties, particularly for its anti-inflammatory and antioxidant effects. However, the bioactive compounds responsible for these effects have not been thoroughly investigated. This study aims to isolate and characterise pentacyclic triterpenoid compounds from M. flexuosa and to evaluate their therapeutic potential. Using various chromatographic and spectroscopic techniques including Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS), three pentacyclic triterpenoid compounds were successfully isolated. Among them, compound 1 (3,11-dioxours-12-en-28-oic acid) exhibited notable bioactivity, significantly inhibiting the activation of Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) (IC50 = 7.39-8.11 µM) and of Nitric Oxide (NO) (IC50 = 4.75-6.59 µM), both of which are key processes in inflammation. Additionally, compound 1 demonstrated potent antioxidant properties by activating the antioxidant enzyme Superoxide Dismutase (SOD) (EC50 = 1.87 µM) and the transcription factor Nuclear factor erythroid 2-related factor 2 (Nrf2) (EC50 = 243-547.59 nM), thus showing its potential in combating oxidative stress. This study is the first to isolate and characterise the three compounds from M. flexuosa, suggesting that compound 1 could be a promising candidate for the development of safer and more effective therapies for inflammatory and oxidative stress-related diseases.

Optimizing next-generation sequencing efficiency in clinical settings: analysis of read length impact on cost and performance.

BACKGROUND: The expansion of sequencing technologies as a result of the response to the COVID-19 pandemic enabled pathogen (meta)genomics to be deployed as a routine component of surveillance in many countries. Scaling genomic surveillance, however, comes with associated costs in both equipment and sequencing reagents, which should be optimized. Here, we evaluate the cost efficiency and performance of different read lengths in identifying pathogens in metagenomic samples. We carefully evaluated performance metrics, costs, and time requirements relative to choices of 75, 150 and 300 base pairs (bp) read lengths in pathogen identification. RESULTS: Our findings revealed that moving from 75 bp to 150 bp read length approximately doubles both the cost and sequencing time. Opting for 300 bp reads leads to approximately two- and three-fold increases, respectively, in cost and sequencing time compared to 75 bp reads. For viral pathogen detection, the sensitivity median ranged from 99% with 75 bp reads to 100% with 150-300 bp reads. However, bacterial pathogens detection was less effective with shorter reads: 87% with 75 bp, 95% with 150 bp, and 97% with 300 bp reads. These findings were consistent across different levels of taxa abundance. The precision of pathogen detection using shorter reads was comparable to that of longer reads across most viral and bacterial taxa. CONCLUSIONS: During disease outbreak situations, when swift responses are required for pathogen identification, we suggest prioritizing 75 bp read lengths, especially if detection of viral pathogens is aimed. This practical approach allows better use of resources, enabling the sequencing of more samples using streamlined workflows, while maintaining a reliable response capability.

Multi-element Contamination and Health Risks in Green Leafy Vegetables from Ambagarh Chowki, Chhattisgarh, India.

Leafy plants are commonly consumed as vegetables in India due to their high nutrient and vitamin content. This study, conducted in Ambagarh Chowki (India), investigated the accumulation potential of 52 elements (including Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Ho, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, P, Pb, Pr, Rb, Sb, Sc, Se, Sm, Sn, Sr, Tb, Te, Th, Ti, Tl, Tm, U, V, W, Y, Yb, and Zn) in seven leafy vegetable species, namely Amaranthus tricolor L., Corchorus olitorius L., Cordia myxa L., Hibiscus sabdariffa L., Ipomoea batatas (L.) Lam., Moringa oleifera Lam., and Spinacia oleracea L. Technique: Inductively coupled plasma mass spectrometry (ICP-MS) was employed for analysis. The maximum concentrations of elements such as Al, Ba, Be, Bi, Cd, Co, Cr, Fe, Ga, Ge, Li, Mn, Ni, Pb, Sb, Th, Tl, U, V, W, and REEs were observed in S. oleracea leaves, indicating their highest accumulation potential. In contrast, the maximum concentrations of As were found in H. sabdariffa leaves; Ca and Si in M. oleifera leaves; Mg, Sr, and Mo in A. tricolor leaves; and P, K, Cu, and Zn in C. myxa leaves, respectively. Twenty-one elements (Cr, Cd, Pb, Ni, Co, V, Cu, Zn, Fe, Mn, Th, Sb, Ba, Be, Li, Sr, Tl, U, Se, Sn, and REEs) exceeded permissible limits set by the WHO. The elevated hazard index values indicated significant non-carcinogenic effects. The sources of these elements could be attributed to a combination of geological factors and agricultural practices. This study highlights the need for further investigation into the potential health implications of consuming these vegetables in the aforementioned region.

Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study.

BACKGROUND: Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes. METHODS: In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017-23). We constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25-April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6-March 1, 2021), for the purposes of model validation. FINDINGS: We found that flights alone transported 79·9 million (95% CI 58·3-101·4 million) passengers annually within Brazil during 2017-22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25-April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6-16, 2021. INTERPRETATION: By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts. Our results unlock the potential of designing country-wide clinical sample collection networks with mobility data-informed approaches, an innovative practice that can improve current surveillance systems. FUNDING: Rockefeller Foundation.

In silico approaches for drug repurposing in oncology: a scoping review.

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Transcriptome profiling reveals distinct alterations in the B-cell signature and dysregulation of peripheral B-cell subsets in sickle cell anemia patients.

Sickle cell anemia (SCA) is characterized by immune system activation and heightened susceptibility to infections. We hypothesized that SCA patients exhibit transcriptional alterations in B-cell-related genes, impacting their peripheral B-cell compartment and leading to dysregulated humoral immunity and increased infection susceptibility. Our objective was to conduct an in silico analysis of whole blood transcriptomes from SCA patients and healthy controls obtained from public repositories. We aimed to identify alterations in the adaptive immune system and validate these findings in our own SCA patient cohort. Bioinformatic analyses unveiled significant transcriptional alterations in B-cell signatures, developmental pathways, and signaling pathways. These results were validated in peripheral blood mononuclear cells from our SCA patient cohort and controls using real-time polymerase chain reaction and flow cytometry. Ninety genes exhibited differential expression, with 70 upregulated and 20 downregulated. Dysregulation in the B-cell compartment of SCA patients was evident, characterized by increased frequencies of immature and naive B-cells, and decreased percentages of memory B-cell subsets compared with healthy controls. Our findings highlight previously unexplored transcriptional and quantitative alterations in peripheral B-cells among SCA patients. Understanding these changes sheds light on the mechanisms contributing to the heightened infection risk in this population. Future studies should delve deeper into these molecular changes to develop targeted interventions and therapeutic strategies aimed at mitigating infection susceptibility in individuals with SCA.

An integrated analysis of the structural changes and gene expression of spleen in human visceral leishmaniasis with and without HIV coinfection.

The spleen plays a pivotal role in the pathogenesis of visceral leishmaniasis. In severe forms of the disease, the spleen undergoes changes that can compromise its function in surveilling blood-circulating pathogens. In this study, we present an integrated analysis of the structural and gene expression alterations in the spleens of three patients with relapsing visceral leishmaniasis, two of whom were coinfected with HIV. Our findings reveal that the IL6 signaling pathway plays a significant role in the disorganization of the white pulp, while BCL10 and ICOSLG are associated with spleen organization. Patients coinfected with HIV and visceral leishmaniasis exhibited lower splenic CD4+ cell density and reduced expression of genes such as IL15. These effects may contribute to a compromised immune response against L. infantum in coinfected individuals, further impacting the structural organization of the spleen.

Contamination, speciation, and health risk assessment of arsenic in leafy vegetables in Ambagarh Chowki (India).

Green leafy vegetables are essential for a balanced diet, providing vital nutrients for overall well-being. However, concerns arise due to contamination with toxic substances, such as arsenic, posing risks to food safety and human health. This study analyzes inorganic (iAs), monomethyl (MMA), and dimethyl arsenic (DMA) in specific leafy vegetables (Amaranthus tricolor L., Corchorus olitorius L., Cordia myxa L., Hibiscus sabdariffa L., Ipomoea batatas (L.) Lam., Moringa oleifera Lam., and Spinacia oleracea L.) grown in the heavily polluted Ambagarh Chouki region, Chhattisgarh, India. Concentrations of DMA, MMA, and iAs ranged from 0 to 155, 0 to 7, and 131 to 3579 mg·kg-1, respectively. The health quotient (HQ) for iAs ranged between 0.37 and 3.78, with an average value of 2.58 ± 1.08.

Emotional regulation and self-perceived quality of life in high-performance mountain sports athletes.

Emotional regulation is an indispensable capacity for human beings, so that alterations in it can lead to the appearance of psychological, social and/or cognitive disorders. Therefore, possessing adequate emotional strategies is intimately related to the quality of life that a person presents. In this sense, high-level athletes suffer constant setbacks and frustrations due to the performance of their sporting activity, in addition to continuous modifications of their daily life activities. Thus, the objective of this research is to explore the emotional regulation and self-perceived quality of life of high-level athletes in mountain sports, analyzing the possible influences of gender, demographic location, body mass index and age. Fifty-four athletes belonging to the High Performance Technification Center of Cáceres (Extremadura, Spain) completed a sociodemographic questionnaire, as well as the Cognitive Emotion Regulation Questionnaire and the WHOQOL-BREF. The Shapiro-Wilkins test was used to analyze the normality of the variables collected and nonparametric statistics were used since the assumption was not met. Both gender and demographic location showed significant differences in the dimensions of the two questionnaires. Likewise, age was associated with the dimensions of both scales, but not body mass index, which was only associated with self-perceived quality of life. In addition, the stepwise linear regression model predicted self-perceived quality of life with a value of 60% across self-culpability, gender body mass index and planning. Therefore, it appears that gender, demographic location, age and body mass index could exert modifications on the levels of emotional regulation and self-perceived quality of life of high-level mountain athletes.

High-throughput prioritization of target proteins for development of new antileishmanial compounds.

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

Nonadiabatic Derivative Couplings through Multiple Franck-Condon Modes Dictate the Energy Gap Law for Near and Short-Wave Infrared Dye Molecules.

Near infrared (NIR, 700-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) dye molecules exhibit significant nonradiative decay rates from the first singlet excited state to the ground state. While these trends can be empirically explained by a simple energy gap law, detailed mechanisms of nearly universal behavior have remained unsettled for many cases. Theoretical and experimental results for two representative NIR/SWIR dye molecules reported here clarify the key mechanism for the observed energy gap law behavior. It is shown that the first derivative nonadiabatic coupling terms serve as major coupling pathways for nonadiabatic decay processes from the first excited singlet state to the ground state for these NIR and SWIR dye molecules and that vibrational modes other than the highest frequency modes also make significant contributions to the rate. This assessment is corroborated by further theoretical comparison with possible alternative mechanisms of intersystem crossing to triplet states and also by comparison with experimental data for deuterated molecules.

Phytochemical study of 'tajy' (Handroanthus impetiginosus (Mart.) Mattos) and 'ka'a oveti' (Luehea divaricata Mart.) extracts.

This study explores the phytochemical composition of leaf extracts from Handroanthus impetiginosus (Mart.) Mattos and Luehea divaricata Mart., used in a contraceptive decoction by Mbya-Guarani women. The phytocompounds were identified by gas chro-matography-mass spectrometry, while Fourier-transform infrared spectroscopy, multi-elemental, and thermal analyses were used to characterise plant biomass. Notably, no phytoconstituent supporting the efficacy of these extracts as female contraceptives was found, except for a small amount (0.3%) of sitosterol. Conversely, L. divaricata leaves contained compounds like 1,3-dihydroxyacetone dimer, N-methyl-N-nitroso-2-propanamine, 2-methoxy-N-(2-methoxyethyl)-N-methyl-ethanamine, and 1,3,5-triazine-2,4,6-triamine, potentially exerting cytotoxic, genotoxic, and toxicogenomic effects. Due to the absence of scientific support for the claimed contraceptive efficacy and the presence of safety concerns, we propose an alternative valorisation pathway centred on the presence of phytochemicals exhibiting antimicrobial activity. This proposition is substantiated by their considerable in vitro efficacy against Botrytis cinerea.

Carvacrol Encapsulation in Chitosan-Carboxymethylcellulose-Alginate Nanocarriers for Postharvest Tomato Protection.

Advancements in polymer science and nanotechnology hold significant potential for addressing the increasing demands of food security, by enhancing the shelf life, barrier properties, and nutritional quality of harvested fruits and vegetables. In this context, biopolymer-based delivery systems present themselves as a promising strategy for encapsulating bioactive compounds, improving their absorption, stability, and functionality. This study provides an exploration of the synthesis, characterization, and postharvest protection applications of nanocarriers formed through the complexation of chitosan oligomers, carboxymethylcellulose, and alginate in a 2:2:1 molar ratio. This complexation process was facilitated by methacrylic anhydride and sodium tripolyphosphate as cross-linking agents. Characterization techniques employed include transmission electron microscopy, energy-dispersive X-ray spectroscopy, infrared spectroscopy, thermal analysis, and X-ray powder diffraction. The resulting hollow nanospheres, characterized by a monodisperse distribution and a mean diameter of 114 nm, exhibited efficient encapsulation of carvacrol, with a loading capacity of approximately 20%. Their suitability for phytopathogen control was assessed in vitro against three phytopathogens-Botrytis cinerea, Penicillium expansum, and Colletotrichum coccodes-revealing minimum inhibitory concentrations ranging from 23.3 to 31.3 µg·mL-1. This indicates a higher activity compared to non-encapsulated conventional fungicides. In ex situ tests for tomato (cv. 'Daniela') protection, higher doses (50-100 µg·mL-1, depending on the pathogen) were necessary to achieve high protection. Nevertheless, these doses remained practical for real-world applicability. The advantages of safety, coupled with the potential for a multi-target mode of action, further enhance the appeal of these nanocarriers.

Phytoconstituents and Ergosterol Biosynthesis-Targeting Antimicrobial Activity of Nutmeg (Myristica fragans Houtt.) against Phytopathogens.

In recent years, nutmeg (Myristica fragans Houtt.) has attracted considerable attention in the field of phytochemistry due to its diverse array of bioactive compounds. However, the potential application of nutmeg as a biorational for crop protection has been insufficiently explored. This study investigated the constituents of a nutmeg hydroethanolic extract via gas chromatography-mass spectrometry and vibrational spectroscopy. The research explored the extract's activity against phytopathogenic fungi and oomycetes, elucidating its mechanism of action. The phytochemical profile revealed fatty acids (including tetradecanoic acid, 9-octadecenoic acid, n-hexadecanoic acid, dodecanoic acid, and octadecanoic acid), methoxyeugenol, and elemicin as the main constituents. Previously unreported phytochemicals included veratone, gelsevirine, and montanine. Significant radial growth inhibition of mycelia was observed against Botrytis cinerea, Colletotrichum acutatum, Diplodia corticola, Phytophthora cinnamomi, and especially against Fusarium culmorum. Mode of action investigation, involving Saccharomyces cerevisiae labeled positively with propidium iodide, and a mutant strain affected in ERG6, encoding sterol C-24 methyltransferase, suggested that the extract induces a necrotic type of death and targets ergosterol biosynthesis. The evidence presented underscores the potential of nutmeg as a source of new antimicrobial agents, showing particular promise against F. culmorum.

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

Early detection of respiratory disease outbreaks through primary healthcare data.

Background: The emergence of coronavirus disease 2019 (COVID-19) in 2020 highlighted the relevance of surveillance systems in detecting early signs of potential outbreaks, thus enabling public health authorities to act before the pathogen becomes widespread. Syndromic digital surveillance through web applications has played a crucial role in monitoring the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, this approach requires expensive infrastructure, which is not available in developing countries. Pre-existing sources of information, such as encounters in primary health care (PHC), can provide valuable data for a syndromic surveillance system. Here we evaluated the utility of PHC data to identify early warning signals of the first COVID-19 outbreak in Bahia-Brazil in 2020. Methods: We compared the weekly counts of PHC encounters due to respiratory complaints and the number of COVID-19 cases in 2020 in Bahia State - Brazil. We used the data from December 2016 to December 2019 to predict the expected number of encounters in 2020. We analysed data aggregated by geographic regions (n = 34) and included those where historical PHC data was available for at least 70% of the population. Results: Twenty-one out of 34 regions met the inclusion criteria. We observed that notification of COVID-19 cases was preceded by at least two weeks with an excess of encounters of respiratory complaints in 18/21 (86%) of the regions analysed and four weeks or more in 10/21 (48%) regions. Conclusions: Digital syndromic surveillance systems based on already established PHC databases may add time to preparedness and response to emerging epidemics.

Critical requirement of SOS1 for tumor development and microenvironment modulation in KRASG12D-driven lung adenocarcinoma.

The impact of genetic ablation of SOS1 or SOS2 is evaluated in a murine model of KRASG12D-driven lung adenocarcinoma (LUAD). SOS2 ablation shows some protection during early stages but only SOS1 ablation causes significant, specific long term increase of survival/lifespan of the KRASG12D mice associated to markedly reduced tumor burden and reduced populations of cancer-associated fibroblasts, macrophages and T-lymphocytes in the lung tumor microenvironment (TME). SOS1 ablation also causes specific shrinkage and regression of LUAD tumoral masses and components of the TME in pre-established KRASG12D LUAD tumors. The critical requirement of SOS1 for KRASG12D-driven LUAD is further confirmed by means of intravenous tail injection of KRASG12D tumor cells into SOS1KO/KRASWT mice, or of SOS1-less, KRASG12D tumor cells into wildtype mice. In silico analyses of human lung cancer databases support also the dominant role of SOS1 regarding tumor development and survival in LUAD patients. Our data indicate that SOS1 is critically required for development of KRASG12D-driven LUAD and confirm the validity of this RAS-GEF activator as an actionable therapeutic target in KRAS mutant LUAD.