No abstract

This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments (MSA) using either the viral protein 1 (VP1; major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds.

Neste trabalho é proposta uma metodologia analítica para extrair 1-nitronaftaleno, 2-nitrofluoreno, 9-nitroantraceno, 3-nitrofluoranteno e 1-nitropireno como compostos modelos para nitropoliaromáticos (nitro-HPA) em amostras aquáticas. A cromatografia líquida capilar com detecção UV on-column (μLC-UV) foi empregada para separar e quantificar todos os cinco nitro-HPA extraídos. Duas técnicas diferentes de extração foram testadas e comparadas: extração líquido-líquido (LLE) e extração em fase sólida (SPE) com dois diferentes tipos de sorbentes: partículas octadecilssilica (C 18 ) e estireno divinilbenzeno (XAD-2). A eluição dos analitos foi testada com diclorometano e acetonitrila. O melhor resultado de recuperação foi obtido para extração SPE -C 18 com diclorometano com valores variando de 76 a 97% e valores de desvio padrão relativo menores que 4%. As condições cromatográficas para análise foram desenvolvidas empregando uma coluna capilar (250 × 0,3 mm) empacotada com partículas C 18 de 5 μm com eluição isocrática de 65% de acetonitrila em água (v/v) com vazão de 7 μL min -1 . Os limites de detecção obtidos no sistema UV on-column (abrangendo de ~3 a 30 μmol L -1 ) são limitantes para uma aplicação ambiental, porém adequados a estudos de toxicidade.In this work, an analytical methodology to extract 1-nitronaphthalene, 2-nitrofluorene, 9-nitroanthracene, 3-nitrofluoranthene, and 1-nitropyrene aquatic samples is proposed. Capillary liquid chromatography with on-column UV detection (μLC-UV) was employed to separate and quantitate all five nitrated polycyclic aromatic hydrocarbons (nitro-PAH) extracted from water samples. Two different extraction techniques were tested and compared: liquid-liquid extraction (LLE) and solid phase extraction (SPE) with two different sorbents, octadecylsilica (C 18 ) and styrene divinylbenzene (XAD-2) particles. The elution of analytes was tested with both acetonitrile and methylene chloride. The best recovery results were obtained for SPE-C 18 extraction with the methylene chloride with values ranging from 76 to 97% with RSD values smaller than 4%. The chromatographic conditions for analysis was carried out using a 250 × 0.3 μm i.d. packed capillary column with 5 μm C 18 particles. The elution was isocratic with 65% acetonitrile in water (v/v) as mobile phase with a flow rate of 7 μL min -1. The detection limits due to on-column UV detection (ranging ~3 -30 μmol L -1 ) are limiting for environmental applications, however, they are suited for toxicological studies.

Cardiovascular diseases are the leading causes of death, and blood pressure (BP) monitoring is essential for prevention, diagnosis, assessment, and treatment. Photoplethysmography (PPG) is a low-cost opto-electronic technique for BP measurement that allows the acquisition of a modulated light signal highly correlated with BP. There are several reports of methods to estimate BP from PPG with impressive results; in this study, we demonstrate that the previous results are excessively optimistic because of their train/test split configuration. To manage this limitation, we considered intra-and inter-subject data arrangements and demonstrated how they affect the results of feature-based BP estimation algorithms (i.e., XGBoost, LightGBM, and CatBoost) and signal-based algorithms (i.e., Residual U-Net, ResNet-18, and ResNet-LSTM). Inter-subject configuration performance is inferior to intra-subject configuration performance, regardless of the model. We also showed that, using only demographic attributes (i.e., age, sex, weight, and subject index number), a regression model achieved results comparable to those obtained in an intrasubject scenario.Although limited to a public clinical database, our findings suggest that algorithms that use an intra-subject setting without a calibration strategy may be learning to identify patients and not predict BP.INDEX TERMS Blood pressure, photoplethysmography, wearables.

Task partitioning in eusocial animals is most likely an evolutionary adaptation that optimizes the efficiency of the colony to grow and reproduce. It was investigated indirect task