Squid – a simple bioinformatics grid

BACKGROUND: BLAST is a widely used genetic research tool for analysis of similarity between nucleotide and protein sequences. This paper presents a software application entitled "Squid" that makes use of grid technology. The current version, as an example, is configured for BLAST applications, but adaptation for other computing intensive repetitive tasks can be easily accomplished in the open source version. This enables the allocation of remote resources to perform distributed computing, making large BLAST queries viable without the need of high-end computers. RESULTS: Most distributed computing / grid solutions have complex installation procedures requiring a computer specialist, or have limitations regarding operating systems. Squid is a multi-platform, open-source program designed to "keep things simple" while offering high-end computing power for large scale applications. Squid also has an efficient fault tolerance and crash recovery system against data loss, being able to re-route jobs upon node failure and recover even if the master machine fails. Our results show that a Squid application, working with N nodes and proper network resources, can process BLAST queries almost N times faster than if working with only one computer. CONCLUSION: Squid offers high-end computing, even for the non-specialist, and is freely available at the project web site. Its open-source and binary Windows distributions contain detailed instructions and a "plug-n-play" instalation containing a pre-configured example

Network analysis of spreading of dengue, Zika and chikungunya in the state of Bahia based on notified, confirmed and discarded cases

Despite successful results of using complex networks to model and characterize the spread of dengue cases, works to date have mainly used data from primarily reported cases, without further consideration whether they were later confirmed or not. On the other hand, a study of the interdependence of confirmed and discarded cases of arboviruses have emphasized that the co-circulation of three arboviruses—dengue, Zika and chikungunya—may have led to false diagnoses due to several similarities in the early symptoms of the three diseases on acute phase. This implies that case notifications of one disease could be confirmed cases of others, and that discarded cases must be taken into account to avoid misinterpretations of the phenomenon. In this work we investigated the consequences of including information from discarded and confirmed cases in the analysis of arbovirus networks. This is done by firstly evaluating the possible changes in the networks after removing the discarded cases from the database of each arbovirus, and secondly by verifying the cross-relationship of the indices of the networks of confirmed and discarded cases of arboviruses. As will be detailed later on, our results reveal changes in the network indices when compared to when only confirmed cases are considered. The magnitudes of the changes are directly proportional to the amount of discarded cases. The results also reveal a strong correlation between the average degree of the networks of discarded cases of dengue and confirmed cases of Zika, but only a moderate correlation between that for networks of discarded cases of dengue and confirmed cases of chikungunya. This finding is compatible with the fact that dengue and Zika diseases are caused by closely related flaviviruses, what is not the case of the chikungunya caused by a togavirus

Complex network analysis of arboviruses in the same geographic domain: Differences and similarities.

Arbovirus can cause diseases with a broad spectrum from mild to severe and long-lasting symptoms, affecting humans worldwide and therefore considered a public health problem with global and diverse socio-economic impacts. Understanding how they spread within and across different regions is necessary to devise strategies to control and prevent new outbreaks. Complex network approaches have widespread use to get important insights on several phenomena, as the spread of these viruses within a given region. This work uses the motif-synchronization methodology to build time varying complex networks based on data of registered infections caused by Zika, chikungunya, and dengue virus from 2014 to 2020, in 417 cities of the state of Bahia, Brazil. The resulting network sets capture new information on the spread of the diseases that are related to the time delay in the synchronization of the time series among different municipalities. Thus the work adds new and important network-based insights to previous results based on dengue dataset in the period 2001-2016. The most frequent synchronization delay time between time series in different cities, which control the insertion of edges in the networks, ranges 7 to 14 days, a period that is compatible with the time of the individual-mosquito-individual transmission cycle of these diseases. As the used data covers the initial periods of the first Zika and chikungunya outbreaks, our analyses reveal an increasing monotonic dependence between distance among cities and the time delay for synchronization between the corresponding time series. The same behavior was not observed for dengue, first reported in the region back in 1986, either in the previously 2001-2016 based results or in the current work. These results show that, as the number of outbreaks accumulates, different strategies must be adopted to combat the dissemination of arbovirus infections

Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation.

Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics

Phakopsora euvitis Causes Unusual Damage to Leaves and Modifies Carbohydrate Metabolism in Grapevine

Asian grapevine rust (Phakopsora euvitis) is a serious disease, which causes severe leaf necrosis and early plant defoliation. These symptoms are unusual for a strict biotrophic pathogen. This work was performed to quantify the effects of P. euvitis on photosynthesis, carbohydrates, and biomass accumulation of grapevine. The reduction in photosynthetic efficiency of the green leaf tissue surrounding the lesions was quantified using the virtual lesion concept (β parameter). Gas exchange and responses of CO2 assimilation to increasing intercellular CO2 concentration were analyzed. Histopathological analyses and quantification of starch were also performed on diseased leaves. Biomass and carbohydrate accumulation were quantified in different organs of diseased and healthy plants. Rust reduced the photosynthetic rate, and β was estimated at 5.78, indicating a large virtual lesion. Mesophyll conductance, maximum rubisco carboxylation rate, and regeneration of ribulose-1,5-bisphosphate dependent on electron transport rate were reduced, causing diffusive and biochemical limitations to photosynthesis. Hypertrophy, chloroplast degeneration of mesophyll cells, and starch accumulation in cells close to lesions were observed. Root carbohydrate concentration was reduced, even at low rust severity. Asian grapevine rust dramatically reduced photosynthesis and altered the dynamics of production and accumulation of carbohydrates, unlike strict biotrophic pathogens. The reduction in carbohydrate reserves in roots would support polyetic damage on grapevine, caused by a polycyclic disease

Inflammatory Response and Activation of Coagulation after COVID-19 Infection

SARS-CoV-2 (COVID-19) infection is responsible for causing a disease with a wide spectrum of clinical presentations. Predisposition to thromboembolic disease due to excessive inflammation is also attributed to the disease. The objective of this study was to characterize the clinical and laboratory aspects of hospitalized patients, in addition to studying the pattern of serum cytokines, and associate them with the occurrence of thromboembolic events. Methodology: A retrospective cohort study with 97 COVID-19 patients hospitalized from April to August 2020 in the Triângulo Mineiro macro-region was carried out. A review of medical records was conducted to evaluate the clinical and laboratory aspects and the frequency of thrombosis, as well as the measurement of cytokines, in the groups that presented or did not present a thrombotic event. Results: There were seven confirmed cases of thrombotic occurrence in the cohort. A reduction in the time of prothrombin activity was observed in the group with thrombosis. Further, 27.8% of all patients had thrombocytopenia. In the group that had thrombotic events, the levels of IL1b, IL-10, and IL2 were higher (p < 0.05). Conclusions: In the studied sample, there was an increase in the inflammatory response in patients with thrombotic events, confirmed by the increase in cytokines. Furthermore, in this cohort, a link was observed between the IL-10 percentage and an increased chance of a thrombotic event

Functional classification of AM-overexpressed transcripts (>10× compared to posterior) from <i>Rhodnius prolixus</i>.

<p>Functional classification of AM-overexpressed transcripts (>10× compared to posterior) from <i>Rhodnius prolixus</i>.</p

Functional classification of gut-overexpressed transcripts (>10× compared to whole body) from <i>Rhodnius prolixus</i>.

<p>Functional classification of gut-overexpressed transcripts (>10× compared to whole body) from <i>Rhodnius prolixus</i>.</p