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Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates’ structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello’s capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users’ expertise, jobs may take minutes or hours to complete.This protocol describes how to design genetic circuits using the web implementation of the Cello 2.0 software, which allows users to construct a DNA sequence for a genetic circuit composed of defined elements and predicts the circuit’s performance in new organisms and genetic contexts.

Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.

Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

The presence, abundance, and distribution of sandflies are strongly influenced by climate and environmental changes. This study aimed to describe the sandfly fauna in an intense transmission area for visceral leishmaniasis and to evaluate the association between the abundance of Lutzomyia longipalpis sensu lato (Lutz & Neiva 1912) (Diptera: Psychodidae) and climatic variables. Captures were carried out 2 yr (July 2017 to June 2019) with automatic light traps in 16 sites of the urban area of Campo Grande, Mato Grosso do Sul state. The temperature (°C), relative humidity (%), precipitation (mm3), and wind speed (km/h) were obtained by a public domain database. TheWilcoxon test compared the absolute frequencies of the species by sex. The association between climatic variables and the absolute frequency of Lu. longipalpis s.l. was assessed using the Spearman's correlation coefficient. A total of 1,572 sandflies into four species were captured. Lutzomyia longipalpis s.l. was the most abundant species and presented a significant correlation with the average temperature, humidity, and wind speed in different periods. Lutzomyia longipalpis s.l. was captured in all months, showing its plasticity in diverse weather conditions.We emphasize the importance of regular monitoring of vectors and human and canine cases, providing data for surveillance and control actions to continue to be carried out in the municipality.

This study reports curcumin as an efficient photolarvicide against Aedes aegypti larvae under natural light illumination. Larval mortality and pupal formation were monitored daily for 21 days under simulated field conditions. In a sucrose-containing formulation, a lethal time 50 (LT50) of 3 days was found using curcumin at 4.6 mg L−1. This formulation promoted no larval toxicity in the absence of illumination, and sucrose alone did not induce larval phototoxicity. The photodegradation byproducts (intermediates) of curcumin were determined and the photodegradation mechanisms proposed. Intermediates with m/z 194, 278, and 370 were found and characterized using LC-MS. The ecotoxicity of the byproducts on non-target organisms (Daphnia, fish, and green algae) indicates that the intermediates do not exhibit any destructive potential for aquatic organisms. The results of photodegradation and ecotoxicity suggest that curcumin is environmentally safe for non-target organisms and, therefore, can be considered for population control of Ae. aegypti.

Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmarking studies on AutoML systems—whose aim is to compare and evaluate their capabilities—have mostly focused on tabular or structured data. In this study, we evaluate AutoML systems on the task of object detection by curating three commonly used object detection datasets (Open Images V7, Microsoft COCO 2017, and Pascal VOC2012) in order to benchmark three different AutoML frameworks—namely, Google’s Vertex AI, NVIDIA’s TAO, and AutoGluon. We reduced the datasets to only include images with a single object instance in order to understand the effect of class imbalance, as well as dataset and object size. We used the metrics of the average precision (AP) and mean average precision (mAP). Solely in terms of accuracy, our results indicate AutoGluon as the best-performing framework, with a mAP of 0.8901, 0.8972, and 0.8644 for the Pascal VOC2012, COCO 2017, and Open Images V7 datasets, respectively. NVIDIA TAO achieved a mAP of 0.8254, 0.8165, and 0.7754 for those same datasets, while Google’s VertexAI scored 0.855, 0.793, and 0.761. We found the dataset size had an inverse relationship to mAP across all the frameworks, and there was no relationship between class size or imbalance and accuracy. Furthermore, we discuss each framework’s relative benefits and drawbacks from the standpoint of ease of use. This study also points out the issues found as we examined the labels of a subset of each dataset. Labeling errors in the datasets appear to have a substantial negative effect on accuracy that is not resolved by larger datasets. Overall, this study provides a platform for future development and research on this nascent field of machine learning.

Sarcopenia is a complex and multifactorial geriatric condition seen in several chronic degenerative diseases. This study aimed to screen for sarcopenia and fall risk in a sample of Parkinson's disease (PD) patients and to investigate demographic and clinical factors associated. This is a cross-sectional study. We evaluated 218 PD patients at the Movement Disorders Clinic in Fortaleza, Brazil, and collected clinical data including experiencing falls in the six months prior to their medical visit. Probable sarcopenia diagnosis was confirmed by using a sarcopenia screening tool (SARC-F questionnaire) and the presence of low muscle strength. One hundred and twenty-one patients (55.5%) were screened positive for sarcopenia using the SARC-F and 103 (47.4%) met the criteria for probable sarcopenia. Disease duration, modified Hoehn and Yahr stage, Schwab and England Activities of Daily Living Scale score, levodopa equivalent dose, probable sarcopenia and positive SARC-F screening were all associated with experiencing falls. Disease duration, lower quality of life and female gender were independently associated with sarcopenia. Experiencing falls was significantly more frequent among patients screened positive in the SARC-F compared to those screened negative. Sarcopenia and PD share common pathways and may affect each other's prognosis and patients' quality of life. Since sarcopenia is associated with lower quality of life and increased risk of falls, active case finding, diagnosis and proper management of sarcopenia in PD patients is essential.

To analyze the effect of age, period, and cohort on suicides among women by hanging, strangulation, suffocation, firearms, and autointoxication in different Brazilian regions from 1980 to 2019. Ecological time-trend study employing estimable functions to estimate APC models, facilitated through the Epi library of the R statistical program, version 4.2.1. Specific rates by age group per 100,00 women and relative risks by period and cohort were estimated using this method. Between 1980 and 2019, 49,997 suicides among women were reported using the methods under study. Higher suicide rates per 100,000 women were observed in the South using strangulation and suffocation (2.42), while lower firearm suicide rates were observed in the Northeast (0.13). After adjusting the APC model, there was an increase in age-specific rates with advancing age across all regions for suicides by hanging, strangulation, and suffocation. In contrast, suicides by firearms and autointoxication showed a decrease in rates with advancing age. The period effect indicated an increased risk of suicides by hanging, strangulation (RR >1 and p<0.05) in the five-year intervals of the 2000s in the North, Southeast, and South regions. During the same period, there was an increased risk of suicides by autointoxication in the Southeast, South, and Northeast (RR>1, p<0.05). Suicides by firearms exhibited a statistically significant reduction in the risk of death from 2005 to 2019 in the Southeast and South regions, and from 2005 to 2014 in the Northeast and Midwest. The observed increase in the North region was not statistically significant (RR>1, p>0.05). The cohort effect demonstrated an increased risk of suicides by hanging, strangulation in younger cohorts (RR>1, p<0.05), whereas other methods showed an elevated risk in older cohorts relative to the 1950-1954 generation. The results presented here may suggest changes in suicide method preferences between 1980 and 2019.

Cells contain thousands of genes. Many genes are involved in the control of cellular activities. Some activities require a few hundred genes to run largely synchronous transcriptional programs. To achieve this, cells have evolved global regulator (GR) proteins that can influence hundreds of genes simultaneously. We have engineered a library of Escherichia coli strains to track the levels over time of these, phenotypically critical, GRs. Each strain has a single-copy plasmid coding for a fast-maturing green fluorescent protein whose transcription is controlled by a copy of the natural GR promoter. By allowing the tracking of GR levels, with sensitivity and specificity, this library should become of wide use in scientific research on bacterial gene expression (from molecular to synthetic biology) and, later, be used in applications in therapeutics and bioindustries.

As part of our continuous studies involving the prospection of natural products from Brazilian flora aiming at the discovery of prototypes for the development of new antiparasitic drugs, the present study describes the isolation of two natural acetylene acetogenins, (2S,3R,4R)-3-hydroxy-4-methyl-2-(n-eicos-11′-yn-19′-enyl)butanolide (1) and (2S,3R,4R)-3-hydroxy-4-methyl-2-(n-eicos-11′-ynyl)butanolide (2), from the seeds of Porcelia macrocarpa (Warm.) R.E. Fries (Annonaceae). Using an ex-vivo assay, compound 1 showed an IC50 value of 29.9 μM against the intracellular amastigote forms of Leishmania (L.) infantum, whereas compound 2 was inactive. These results suggested that the terminal double bond plays an important role in the activity. This effect was also observed for the semisynthetic acetylated (1a and 2a) and eliminated (1b and 2b) derivatives, since only compounds containing a double bond at C-19 displayed activity, resulting in IC50 values of 43.3 μM (1a) and 23.1 μM (1b). In order to evaluate the effect of the triple bond in the antileishmanial potential, the mixture of compounds 1 + 2 was subjected to catalytic hydrogenation to afford a compound 3 containing a saturated side chain. The antiparasitic assays performed with compound 3, acetylated (3a), and eliminated (3b) derivatives confirmed the lack of activity. Furthermore, an in-silico study using the SwissADME online platform was performed to bioactive compounds 1, 1a, and 1b in order to investigate their physicochemical parameters, pharmacokinetics, and drug-likeness. Despite the reduced effect against amastigote forms of the parasite to the purified compounds, different mixtures of compounds 1 + 2, 1a + 2a, and 1b + 2b were prepared and exhibited IC50 values ranging from 7.9 to 38.4 μM, with no toxicity for NCTC mammalian cells (CC50 > 200 μM). Selectivity indexes to these mixtures ranged from >5.2 to >25.3. The obtained results indicate that seeds of Porcelia macrocarpa are a promising source of interesting prototypes for further modifications aiming at the discovery of new antileishmanial drugs.