The air pollution caused by particulate matter (PM) has become a public health issue due to the risks to human life and the environment. The PM concentration in the air causes haze and affects the ...lungs and the heart, leading to reduced visibility, allergic reactions, pneumonia, asthma, cardiopulmonary diseases, lung cancer, and even death. In this context, the development of systems for monitoring, forecasting, and controlling emissions plays an important role. The literature about forecasting systems based on Artificial Neural Networks (ANNs) ensembles has been highlighted regarding statistical accuracy and efficiency. In this article, trainable and non-trainable combination methods are used for PM 10 and PM 2.5 (particles with an aerodynamic diameter less than 10 and 2.5 micrometers, respectively) time series forecasting for eight different locations, in Finland and Brazil, for different periods. Trainable ensembles based on ANNs, linear regression, and Copulas are compared with non-trainable combinations (mean and median), single ANNs, and linear statistical approaches. Different models are considered so far, including Autoregressive model (AR), Autoregressive and Moving Average Model (ARMA), Infinite Impulse Response Filters (IIR), Multilayer Perceptron (MLP), Radial Basis Function Networks (RBF), Extreme Learning Machines (ELM), Echo State Networks (ESN), and Adaptive Network Fuzzy Inference System (ANFIS). The use of ANNs ensembles, mainly combined with MLP, leads to a better one step ahead forecasting performance. The use of robust air pollution forecasting tools is prime to assist governments in managing air pollution issues like hospital collapse during adverse air quality situations. In this sense, our study is indirectly related to the following United Nations sustainable development goals: SDG 3 - good health and well-being and SDG 11 - sustainable cities and communities.
Hevea brasiliensis is an important commercial crop due to the high quality of the latex it produces; however, little is known about viral infections in this plant. The only virus described to infect ...H. brasiliensis until now is a Carlavirus, which was described more than 30 years ago. Virus-derived small interfering RNA (vsiRNAs) are the product of the plant's antiviral defense triggered by dsRNA viral intermediates generated, during the replication cycle. These vsiRNAs are complementar to viral genomes and have been widely used to identify and characterize viruses in plants.
In the present study, we investigated the virome of leaf and sapwood samples from native H. brasiliensis trees collected in two geographic areas in the Brazilian Amazon. Small RNA (sRNA) deep sequencing and bioinformatic tools were used to assembly, identify and characterize viral contigs. Subsequently, PCR amplification techniques were performed to experimentally verify the presence of the viral sequences. Finally, the phylogenetic relationship of the putative new virus with related viral genomes was analyzed.
Our strategy allowed the identification of 32 contigs with high similarity to viral reference genomes, from which 23 exhibited homology to viruses of the Tymoviridae family. The reads showed a predominant size distribution at 21 nt derived from both strands, which was consistent with the vsiRNAs profile. The presence and genome position of the viral contigs were experimentally confirmed using droplet digital PCR amplifications. A 1913 aa long fragment was obtained and used to infer the phylogenetic relationship of the putative new virus, which indicated that it is taxonomically related to the Grapevine fleck virus, genus Maculavirus. The putative new virus was named Hevea brasiliensis virus (HBrV) in reference to its host.
The methodological strategy applied here proved to be efficient in detecting and confirming the presence of new viral sequences on a 'very difficult to manage' sample. This is the second time that viral sequences, that could be ascribed as a putative novel virus, associated to the rubber tree has been identified.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Low‐pass whole genome sequencing (LP‐WGS) has been applied as alternative method to detect copy number variants (CNVs) in the clinical setting. Compared with chromosomal microarray analysis ...(CMA), the sequencing‐based approach provides a similar resolution of CNV detection at a lower cost. In this study, we assessed the efficiency and reliability of LP‐WGS as a more affordable alternative to CMA. A total of 1363 patients with unexplained neurodevelopmental delay/intellectual disability, autism spectrum disorders, and/or multiple congenital anomalies were enrolled. Those patients were referred from 15 nonprofit organizations and university centers located in different states in Brazil. The analysis of LP‐WGS at 1x coverage (>50kb) revealed a positive testing result in 22% of the cases (304/1363), in which 219 and 85 correspond to pathogenic/likely pathogenic (P/LP) CNVs and variants of uncertain significance (VUS), respectively. The 16% (219/1363) diagnostic yield observed in our cohort is comparable to the 15%–20% reported for CMA in the literature. The use of commercial software, as demonstrated in this study, simplifies the implementation of the test in clinical settings. Particularly for countries like Brazil, where the cost of CMA presents a substantial barrier to most of the population, LP‐WGS emerges as a cost‐effective alternative for investigating copy number changes in cytogenetics.
The non-classical histocompatibility antigen G (HLA-G) is an immune checkpoint molecule that has been implicated in viral disorders. We evaluated the plasma soluble HLA-G (sHLA-G) in 239 individuals, ...arranged in COVID-19 patients (n = 189) followed up at home or in a hospital, and in healthy controls (n = 50). Increased levels of sHLA-G were observed in COVID-19 patients irrespective of the facility care, gender, age, and the presence of comorbidities. Compared with controls, the sHLA-G levels increased as far as disease severity progressed; however, the levels decreased in critically ill patients, suggesting an immune exhaustion phenomenon. Notably, sHLA-G exhibited a positive correlation with other mediators currently observed in the acute phase of the disease, including IL-6, IL-8 and IL-10. Although sHLA-G levels may be associated with an acute biomarker of COVID-19, the increased levels alone were not associated with disease severity or mortality due to COVID-19. Whether the SARS-CoV-2 per se or the innate/adaptive immune response against the virus is responsible for the increased levels of sHLA-G are questions that need to be further addressed.
Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. ...Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities.
Evapotranspiration (ET) is the main driver of the energy balance partitioning and influences hydrological and carbon cycles at global, regional and local scales. Furthermore, it is the main ...requirement for developing strategies to improve water use in agriculture. It is known that there is a close relationship between ET and rainfall, especially in tropical environments. Thus, the main goal of this article was to evaluate how ET and its controls (surface conductance - Gs; decoupling coefficient - Ω; Priestley-Taylor parameter - α) respond to the seasonal variability of meteorological forcing in tropical grazed pastures under the climatic conditions of Northeast Brazil. ET was measured using an Eddy covariance (EC) system and analyzed based on data from two agricultural years (2015–2016 and 2016–2017) with negative (-59 mm) and positive (356 mm) rainfall anomaly, respectively. ET exhibited pronounced seasonality, closely aligned with the seasonality of rainfall. Lower daily averages were observed during the dry season in both agricultural years (1.01 ± 0.60 and 0.89 ± 0.44 mm, respectively). On the other hand, higher daily averages were observed during the rainy season (2.44 ± 0.75 and 4.83 ± 0.96 mm, respectively). The Gs patterns and the significant correlation between Gs, Ω, and α (p < 0.01) indicate that surface control prevails over atmospheric control on an annual scale. This finding is confirmed by the annual mean values of Ω (0.27, 2015–2016) and α (0.38, 2016–2017). This relationship is likely induced by a vegetative stomatal control mechanism, which protects the vegetation against excessive water loss during periods of high temperatures and low humidity levels. These findings are crucial for understanding how droughts modulate components of the energy balance and water fluxes in pastures, especially given the intensification of these events. This has implications for the implementation of climate change mitigation policies and soil management.
•The seasonality of grazing pasture ET was in line with the seasonality of rain.•On an annual scale, surface control over ET is more relevant than atmospheric control.•VPD is the main controlling factor of surface conductance.
Background
Dermatofibrosarcoma protuberans (DFSP) is a rare low grade tumor with a locally aggressive behavior and low metastatic potential.
Objectives
To evaluate the factors that are associated ...with relapse in DFSP.
Methods Retrospective analysis of medical records from 61 patients with dermatofibrosarcoma. Fluorescence in situ hybridization was used to detect translocations.
Results
Of 61 patients, 6 experienced a relapse. No patient with resection margins greater than 3 cm had a recurrence. One relapse was observed in a patient treated with at least 2 cm margins and 4 relapses occurred in 16 patients whose margins were below 2 cm (P = 0.018). The frequency of translocations was 77.8%. The recurrence rate was lower in patients with translocation, but this difference was not significant. Immunohistochemical markers did not correlate with recurrence rates, but greater FasL expression was associated with recurrence in patients with margins smaller than 3 cm.
Conclusions
Surgical margins smaller than than 2 cm are related to higher recurrences in dermatofibrosarcomas. In this analysis a 2 cm margin was acceptable for treatment. Between all the immunohistochemical markers analyzed, only FasL was associated with a higher recurrence rate in patients with margins smaller than 3 cm.
Preliminary methodologically limited studies suggested that taste and smell known as chemosensory impairments and neuropsychiatric symptoms are associated in post-COVID-19. The objective of this ...study is to evaluate whether chemosensory dysfunction and neuropsychiatric impairments in a well-characterized post-COVID-19 sample. This is a cohort study assessing adult patients hospitalized due to moderate or severe forms of COVID-19 between March and August 2020. Baseline information includes several clinical and hospitalization data. Further evaluations were made using several different reliable instruments designed to assess taste and smell functions, parosmia, and neuropsychiatric disorders (using standardized psychiatric and cognitive measures). Out of 1800 eligible individuals, 701 volunteers were assessed on this study. After multivariate analysis, patients reporting parosmia had a worse perception of memory performance (
p
< 0.001). Moderate/severe hypogeusia was significantly associated with a worse performance on the word list memory task (
p
= 0.012); Concomitant moderate/severe olfactory and gustatory loss during the acute phase of COVID-19 was also significantly associated with episodic memory impairment (
p
= 0.006). We found a positive association between reported chemosensory (taste and olfaction) abnormalities and cognition dysfunction in post-COVID-19 patients. These findings may help us identify potential mechanisms linking these two neurobiological functions, and also support the speculation on a possible route through which SARS-CoV-2 may reach the central nervous system.
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