"Bunch of acai berries sign": a new radiological sign in patients with CNS involvement in Chagas disease.

Chagas disease is an infection caused by Trypanosoma cruzi, a parasite endemic in Latin America. Acute involvement of the CNS by Chagas has been considered rare, but presumed reactivation of chronic disease in immunosuppressed patients has been the subject of recent reports. Our objective is to describe the clinical and imaging characteristics of four patients with Chagas disease and CNS involvement, and the patients had to have available MRI and a diagnosis confirmed by biopsy. The imaging findings were similar, highlighting the presence of focal cerebral lesions with hypointensity on T2-WI, and these lesions assume a "bunch of acai berries appearance", a fruit involved in the transmission of T. cruzi. The post Gd T1-WI shows punctate enhancement. Knowledge of this pattern may be crucial to recognize this disease in immunocompromised patients from endemic areas.

Late Quaternary hydroclimate and vegetation changes in an upland lake in southeastern Amazonia.

An integrated approach considering facies, isotopic, and palynological analyses of lake sediments from the Serra Norte de Carajás, southeastern Amazonia, is presented in this work to refine paleoclimate and paleohydrological changes based on upland lake sediments during the late Quaternary. The sediments have a fining-upward deposition cycle typical of upland swamps/lakes. The origin of organic matter is autochthonous mainly related to C3 terrestrial plants, macrophytes and algae. The pollen records of Hedyosmum during the Early Pleistocene suggest lower temperatures than those observed along Holocene. In the transitional period between the Pleistocene and the Holocene, rainfall decreased, causing the retraction of the flooded area, favoring the development of marshy conditions. The Late and Middle Holocene were marked by higher temperatures and lower humidity. Afterward, the increased pollen concentration from canga and forest vegetation, macrophytes, palms, and algae suggested increased humidity in the Early Holocene. The relative contribution of forest pollen along the records indicated that drier conditions were not strong enough for an extensive expansion of canga over forested areas.

geneRFinder: gene finding in distinct metagenomic data complexities.

BACKGROUND: Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. RESULTS: We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar's test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. CONCLUSIONS: We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/ , and also we provide a novel, comprehensive benchmark data for gene prediction-which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions-available at https://sourceforge.net/p/generfinder-benchmark .

Machine learning meets genome assembly.

MOTIVATION: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. RESULTS: This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers-particularly the ones that use machine learning-to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.

PIPEBAR and OverlapPER: tools for a fast and accurate DNA barcoding analysis and paired-end assembly.

BACKGROUND: Taxonomic identification of plants and insects is a hard process that demands expert taxonomists and time, and it's often difficult to distinguish on morphology only. DNA barcodes allow a rapid species discovery and identification and have been widely used for taxonomic identification by targeting known gene regions that permit to discriminate these species. DNA barcode sequence analysis is usually carried out with processes and tools that still demand a high interaction with the user or researcher. To reduce at most such interaction, we proposed PIPEBAR, a pipeline for DNA chromatograms analysis of Sanger platform sequencing, ensuring high quality consensus sequences along with efficient running time. We also proposed a paired-end reads assembly tool, OverlapPER, which is used in sequence or independently of PIPEBAR. RESULTS: PIPEBAR is a command line tool to automatize the processing of large number of trace files. It is accurate as the proprietary Geneious tool and faster than most popular software for barcoding analysis. It is 7 times faster than Geneious and 14 times faster than SeqTrace for processing hundreds of barcoding sequences. OverlapPER is a novel tool for overlapping paired-end reads accurately that accepts both substitution and indel errors and returns both overlapped and non-overlapped regions between a pair of reads. OverlapPER obtained the best results compared to currently used tools when merging 1,000,000 simulated paired-end reads. CONCLUSIONS: PIPEBAR and OverlapPER run on most operating systems and are freely available, along with supporting code and documentation, at https://sourceforge.net/projects/PIPEBAR / and https://sourceforge.net/projects/overlapper-reads /.

Tally: a scoring tool for boundary determination between repetitive and non-repetitive protein sequences.

MOTIVATION: Tandem Repeats (TRs) are abundant in proteins, having a variety of fundamental functions. In many cases, evolution has blurred their repetitive patterns. This leads to the problem of distinguishing between sequences that contain highly imperfect TRs, and the sequences without TRs. The 3D structure of proteins can be used as a benchmarking criterion for TR detection in sequences, because the vast majority of proteins having TRs in sequences are built of repetitive 3D structural blocks. According to our benchmark, none of the existing scoring methods are able to clearly distinguish, based on the sequence analysis, between structures with and without 3D TRs. RESULTS: We developed a scoring tool called Tally, which is based on a machine learning approach. Tally is able to achieve a better separation between sequences with structural TRs and sequences of aperiodic structures, than existing scoring procedures. It performs at a level of 81% sensitivity, while achieving a high specificity of 74% and an Area Under the Receiver Operating Characteristic Curve of 86%. Tally can be used to select a set of structurally and functionally meaningful TRs from all TRs detected in proteomes. The generated dataset is available for benchmarking purposes. AVAILABILITY AND IMPLEMENTATION: Source code is available upon request. Tool and dataset can be accessed through our website: http://bioinfo.montp.cnrs.fr/?r=Tally CONTACT: andrey.kajava@crbm.cnrs.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The quest for the reliability of machine learning models in binary classification on tabular data.

In this paper we explore the reliability of contexts of machine learning (ML) models. There are several evaluation procedures commonly used to validate a model (precision, F1 Score and others); However, these procedures are not linked to the evaluation of learning itself, but only to the number of correct answers presented by the model. This characteristic makes it impossible to assess whether a model was able to learn through elements that make sense of the context in which it is inserted. Therefore, the model could achieves good results in the training stage but poor results when the model needs to be generalized. When there are many different models that achieve similar performance, the model that presented the highest number of hits in training does not mean that this model is the best. Therefore, we created a methodology based on Item Response Theory that allows us to identify whether an ML context is unreliable, providing an extra and different validation for ML models.

Gene association analysis: a survey of frequent pattern mining from gene expression data.

Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.

Quantitative computed tomography and cranial burr holes: a model to evaluate the quality of cranial reconstruction in humans.

Current methods to evaluate the biologic development of bone grafts in human beings do not quantify results accurately. Cranial burr holes are standardized critical bone defects, and the differences between bone powder and bone grafts have been determined in numerous experimental studies. This study evaluated quantitative computed tomography (QCT) as a method to objectively measure cranial bone density after cranial reconstruction with autografts. In each of 8 patients, 2 of 4 surgical burr holes were reconstructed with autogenous wet bone powder collected during skull trephination, and the other 2 holes, with a circular cortical bone fragment removed from the inner table of the cranial bone flap. After 12 months, the reconstructed areas and a sample of normal bone were studied using three-dimensional QCT; bone density was measured in Hounsfield units (HU). Mean (SD) bone density was 1535.89 (141) HU for normal bone (P < 0.0001), 964 (176) HU for bone fragments, and 453 (241) HU for bone powder (P < 0.001). As expected, the density of the bone fragment graft was consistently greater than that of bone powder. Results confirm the accuracy and reproducibility of QCT, already demonstrated for bone in other locations, and suggest that it is an adequate tool to evaluate cranial reconstructions. The combination of QCT and cranial burr holes is an excellent model to accurately measure the quality of new bone in cranial reconstructions and also seems to be an appropriate choice of experimental model to clinically test any cranial bone or bone substitute reconstruction.

PipeCoV: a pipeline for SARS-CoV-2 genome assembly, annotation and variant identification.

Motivation: Since the identification of the novel coronavirus (SARS-CoV-2), the scientific community has made a huge effort to understand the virus biology and to develop vaccines. Next-generation sequencing strategies have been successful in understanding the evolution of infectious diseases as well as facilitating the development of molecular diagnostics and treatments. Thousands of genomes are being generated weekly to understand the genetic characteristics of this virus. Efficient pipelines are needed to analyze the vast amount of data generated. Here we present a new pipeline designed for genomic analysis and variant identification of the SARS-CoV-2 virus. Results: PipeCoV shows better performance when compared to well-established SARS-CoV-2 pipelines, with a lower content of Ns and higher genome coverage when compared to the Wuhan reference. It also provides a variant report not offered by other tested pipelines. Availability: https://github.com/alvesrco/pipecov.

New plastomes of eight Ipomoea species and four putative hybrids from Eastern Amazon.

Ipomoea is a large pantropical genus globally distributed, which importance goes beyond the economic value as food resources or ornamental crops. This highly diverse genus has been the focus of a great number of studies, enriching the plant genomics knowledge, and challenging the plant evolution models. In the Carajás mountain range, located in Eastern Amazon, the savannah-like ferruginous ecosystem known as canga harbors highly specialized plant and animal populations, and Ipomoea is substantially representative in such restrictive habitat. Thus, to provide genetic data and insights into whole plastome phylogenetic relationships among key Ipomoea species from Eastern Amazon with little to none previously available data, we present the complete plastome sequences of twelve lineages of the genus, including the canga microendemic I. cavalcantei, the closely related I. marabaensis, and their putative hybrids. The twelve plastomes presented similar gene content as most publicly available Ipomoea plastomes, although the putative hybrids were correctly placed as closely related to the two parental species. The cavalcantei-marabaensis group was consistently grouped between phylogenetic methods. The closer relationship of the I. carnea plastome with the cavalcantei-marabaensis group, as well as the branch formed by I. quamoclit, I. asarifolia and I. maurandioides, were probably a consequence of insufficient taxonomic representativity, instead of true genetic closeness, reinforcing the importance of new plastome assemblies to resolve inconsistencies and boost statistical confidence, especially the case for South American clades of Ipomoea. The search for k-mers presenting high dispersion among the frequency distributions pointed to highly variable coding and intergenic regions, which may potentially contribute to the genetic diversity observed at species level. Our results contribute to the resolution of uncertain clades within Ipomoea and future phylogenomic studies, bringing unprecedented results to Ipomoea species with restricted distribution, such as I. cavalcantei.

Draft Genome Sequence of the Novel, Moderately Thermophilic, Iron- and Sulfur-Oxidizing Firmicute Strain Y002, Isolated from an Extremely Acidic Geothermal Environment.

We report the draft genome sequence of the Firmicute strain Y002, a facultatively anaerobic, acidophilic bacterium that catalyzes the dissimilatory oxidation of iron and sulfur and the reduction of ferric iron. Analysis of the genome (2.9 Mb; G+C content, 46 mol%) provided insights into its ability to grow in extremely acidic geothermal environments.

Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration.

Despite the importance of climate-adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron-rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype-phenotype-environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site-adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.

Conservation implications of genetic structure in the narrowest endemic quillwort from the Eastern Amazon.

The quillwort Isoëtes cangae is a critically endangered species occurring in a single lake in Serra dos Carajás, Eastern Amazon. Low genetic diversity and small effective population sizes (N e) are expected for narrow endemic species (NES). Conservation biology studies centered in a single species show some limitations, but they are still useful considering the limited time and resources available for protection of species at risk of extinction. Here, we evaluated the genetic diversity, population structure, N e, and minimum viable population (MVP) of I. cangae to provide information for effective conservation programs. Our analyses were based on 55 individuals collected from the Amendoim Lake and 35,638 neutral SNPs. Our results indicated a single panmictic population, moderate levels of genetic diversity, and N e in the order of thousands, contrasting the expected for NES. Negative FIS values were also found, suggesting that I. cangae is not under risk of inbreeding depression. Our findings imply that I. cangae contains enough genetic diversity to ensure evolutionary potential and that all individuals should be treated as one demographic unit. These results provide essential information to optimize ex situ conservation efforts and genetic diversity monitoring, which are currently applied to guide I. cangae conservation plans.

Unraveling the plant diversity of the Amazonian canga through DNA barcoding.

The canga of the Serra dos Carajás, in Eastern Amazon, is home to a unique open plant community, harboring several endemic and rare species. Although a complete flora survey has been recently published, scarce to no genetic information is available for most plant species of the ironstone outcrops of the Serra dos Carajás. In this scenario, DNA barcoding appears as a fast and effective approach to assess the genetic diversity of the Serra dos Carajás flora, considering the growing need for robust biodiversity conservation planning in such an area with industrial mining activities. Thus, after testing eight different DNA barcode markers (matK, rbcL, rpoB, rpoC1, atpF-atpH, psbK-psbI, trnH-psbA, and ITS2), we chose rbcL and ITS2 as the most suitable markers for a broad application in the regional flora. Here we describe DNA barcodes for 1,130 specimens of 538 species, 323 genera, and 115 families of vascular plants from a highly diverse flora in the Amazon basin, with a total of 344 species being barcoded for the first time. In addition, we assessed the potential of using DNA metabarcoding of bulk samples for surveying plant diversity in the canga. Upon achieving the first comprehensive DNA barcoding effort directed to a complete flora in the Brazilian Amazon, we discuss the relevance of our results to guide future conservation measures in the Serra dos Carajás.

A metagenomic survey of soil microbial communities along a rehabilitation chronosequence after iron ore mining.

Microorganisms are useful environmental indicators, able to deliver essential insights to processes regarding mine land rehabilitation. To compare microbial communities from a chronosequence of mine land rehabilitation to pre-disturbance levels from references sites covered by native vegetation, we sampled non-rehabilitated, rehabilitating and reference study sites from the Urucum Massif, Southwestern Brazil. From each study site, three composed soil samples were collected for chemical, physical, and metagenomics analysis. We used a paired-end library sequencing technology (NextSeq 500 Illumina); the reads were assembled using MEGAHIT. Coding DNA sequences (CDS) were identified using Kaiju in combination with non-redundant NCBI BLAST reference sequences containing archaea, bacteria, and viruses. Additionally, a functional classification was performed by EMG v2.3.2. Here, we provide the raw data and assembly (reads and contigs), followed by initial functional and taxonomic analysis, as a base-line for further studies of this kind. Further investigation is needed to fully understand the mechanisms of environmental rehabilitation in tropical regions, inspiring further researchers to explore this collection for hypothesis testing.

On the utilization of deep and ensemble learning to detect milk adulteration.

BACKGROUND: Fraudulent milk adulteration is a dangerous practice in the dairy industry that is harmful to consumers since milk is one of the most consumed food products. Milk quality can be assessed by Fourier Transformed Infrared Spectroscopy (FTIR), a simple and fast method for obtaining its compositional information. The spectral data produced by this technique can be explored using machine learning methods, such as neural networks and decision trees, in order to create models that represent the characteristics of pure and adulterated milk samples. RESULTS: Thousands of milk samples were collected, some of them were manually adulterated with five different substances and subjected to infrared spectroscopy. This technique produced spectral data from the milk samples composition, which were used for training different machine learning algorithms, such as deep and ensemble decision tree learners. The proposed method is used to predict the presence of adulterants in a binary classification problem and also the specific assessment of which of five adulterants was found through multiclass classification. In deep learning, we propose a Convolutional Neural Network architecture that needs no preprocessing on spectral data. Classifiers evaluated show promising results, with classification accuracies up to 98.76%, outperforming commonly used classical learning methods. CONCLUSIONS: The proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry.

Landscape genomics to the rescue of a tropical bee threatened by habitat loss and climate change.

Habitat degradation and climate change are currently threatening wild pollinators, compromising their ability to provide pollination services to wild and cultivated plants. Landscape genomics offers powerful tools to assess the influence of landscape modifications on genetic diversity and functional connectivity, and to identify adaptations to local environmental conditions that could facilitate future bee survival. Here, we assessed range-wide patterns of genetic structure, genetic diversity, gene flow, and local adaptation in the stingless bee Melipona subnitida, a tropical pollinator of key biological and economic importance inhabiting one of the driest and hottest regions of South America. Our results reveal four genetic clusters across the species' full distribution range. All populations were found to be under a mutation-drift equilibrium, and genetic diversity was not influenced by the amount of reminiscent natural habitats. However, genetic relatedness was spatially autocorrelated and isolation by landscape resistance explained range-wide relatedness patterns better than isolation by geographic distance, contradicting earlier findings for stingless bees. Specifically, gene flow was enhanced by increased thermal stability, higher forest cover, lower elevations, and less corrugated terrains. Finally, we detected genomic signatures of adaptation to temperature, precipitation, and forest cover, spatially distributed in latitudinal and altitudinal patterns. Taken together, our findings shed important light on the life history of M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Conservation actions such as restricting long-distance colony transportation, preserving local adaptations, and improving the connectivity between highlands and lowlands are likely to assure future pollination services.

Differential accumulation of proteins in oil palms affected by fatal yellowing disease.

There is still no consensus on the true origin of fatal yellowing, one of the most important diseases affecting oil palm (Elaeis guineensis Jacq.) plantations. This study involved two-dimensional liquid chromatography coupled with tandem mass spectrometry (2D-UPLC-MSE) analyses to identify changes in protein profiles of oil palms affected by FY disease. Oil palm roots were sampled from two growing areas. Differential accumulation of proteins was assessed by comparing plants with and without symptoms and between plants at different stages of FY development. Most of the proteins identified with differential accumulation were those related to stress response and energy metabolism. The latter proteins include the enzymes alcohol dehydrogenase and aldehyde dehydrogenase, related to alcohol fermentation, which were identified in plants with and without symptoms. The presence of these enzymes suggests an anaerobic condition before or during FY. Transketolase, isoflavone reductase, cinnamyl alcohol dehydrogenase, caffeic acid 3-O-methyltransferase, S-adenosylmethionine synthase, aldehyde dehydrogenase and ferritin, among others, were identified as potential marker proteins and could be used to guide selection of FY-tolerant oil palm genotypes or to understand the source of this anomaly. When comparing different stages of FY, we observed high accumulation of alcohol dehydrogenase and other abiotic stress related-proteins at all disease stages. On the other hand, biological stress-related proteins were more accumulated at later stages of the disease. These results suggest that changes in abiotic factors can trigger FY development, creating conditions for the establishment of opportunistic pathogens.

Influence of water quality on diversity and composition of fungal communities in a tropical river.

Freshwater fungi are key decomposers of organic material and play important roles in nutrient cycling, bio-remediation and ecosystem functioning. Although aquatic fungal communities respond to pollution, few studies have quantitatively assessed the effect of freshwater contamination on fungal diversity and composition; and knowledge is scarcer for tropical systems. Here we help fill this knowledge gap by studying a heavily-contaminated South American river spanning a biodiversity hotspot. We collected 30 water samples scattered across a quality gradient over two seasons and analyzed them using Terminal Restriction Fragment Length Polymorphisms (T-RFLP) coupled with 454 Pyrosequencing. Using T-RFLP we identified 451 and 442 Operational Taxonomy Units (OTUs) in the dry and rainy seasons respectively, whereas Pyrosequencing revealed 48,553 OTUs from which 11% were shared between seasons. Although 68% of all identified OTUs and 51% of all identified phyla remained unidentified, dominant fungal phyla included the Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota, Zygomycota and Neocallimastigomycota, while Calcarisporiella, Didymosphaeria, Mycosphaerella (Ascomycota) and Rhodotorula (Basidiomycota) were the most abundant genera. Fungal diversity was affected by pH and dissolved iron, while community composition was influenced by dissolved oxygen, pH, nitrate, biological oxygen demand, total aluminum, total organic carbon, total iron and seasonality. The presence of potentially pathogenic species was associated with high pH. Furthermore, geographic distance was positively associated with community dissimilarity, suggesting that local conditions allowed divergence among fungal communities. Overall, our findings raise potential concerns for human health and the functioning of tropical river ecosystems and they call for improved water sanitation systems.