Potential PCR amplification bias in identifying complex ecological patterns: Higher species compositional homogeneity revealed in smaller-size coral reef zooplankton by metatranscriptomics.

PCR-based high-throughput sequencing has permitted comprehensive resolution analyses of zooplankton diversity dynamics. However, significant methodological issues still surround analyses of complex bulk community samples, not least as in prevailing PCR-based approaches. Marine drifting animals-zooplankton-play essential ecological roles in the pelagic ecosystem, transferring energy and elements to higher trophic levels, such as fishes, cetaceans and others. In the present study, we collected 48 size-fractionated zooplankton samples in the vicinity of a coral reef island with environmental gradients. To investigate the spatiotemporal dynamics of zooplankton diversity patterns and the effect of PCR amplification biases across these complex communities, we first took metatranscriptomics approach. Comprehensive computational analyses revealed a clear pattern of higher/lower homogeneity in smaller/larger zooplankton compositions across samples respectively. Our study thus suggests changes in the role of dispersal across the sizes. Next, we applied in silico PCR to the metatranscriptomics datasets, in order to estimate the extent of PCR amplification bias. Irrespective of stringency criteria, we observed clear separations of size fraction sample clusters in both metatranscriptomics and in silico datasets. In contrast, the pattern-smaller-fractioned communities had higher compositional homogeneity than larger ones-was observed in the metatranscriptomics data but not in the in silico datasets. To investigate this discrepancy further, we analysed the mismatches of widely used mitochondrial CO1 primers and identified priming site mismatches likely driving PCR-based biases. Our results suggest the use of metatranscriptomics or, although less ideal, redesigning the CO1 primers is necessary to circumvent these issues.

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy.

Thermal treatments that use ultrasound devices as a tool have as a key point the temperature control to be applied in a specific region of the patient's body. This kind of procedure requires caution because the wrong regulation can either limit the treatment or aggravate an existing injury. Therefore, determining the temperature in a region of interest in real-time is a subject of high interest. Although this is still an open problem, in the field of ultrasound analysis, the use of machine learning as a tool for both imaging and automated diagnostics are application trends. In this work, a data-driven approach is proposed to address the problem of estimating the temperature in regions of a B-mode ultrasound image as a supervised learning problem. The proposal consists in presenting a novel data modeling for the problem that includes information retrieved from conventional B-mode ultrasound images and a parametric image built based on changes in backscattered energy (CBE). Then, we compare the performance of classic models in the literature. The computational results presented that, in a simulated scenario, the proposed approach that a Gradient Boosting model would be able to estimate the temperature with a mean absolute error of around 0.5°C, which is acceptable in practical environments both in physiotherapic treatments and high intensity focused ultrasound (HIFU).

Diversity and Ecology of Lobophora Species Associated with Coral Reef Systems in the Western Gulf of Thailand, including the Description of Two New Species.

The brown macroalgal genus Lobophora plays important ecological roles in many marine ecosystems. This group has received much attention over the past decade, and a considerable number of new species have been identified globally. However, our knowledge of the genus diversity and ecology along south-east Asian coasts are still limited. Given the growing body of research that uses a combination of molecular and morphological data to identify cryptic species, this study investigates the diversity of Lobophora in the western Gulf of Thailand using morphological and molecular data, as well as their interactions with scleractinian corals. A total of 36 Lobophora specimens were collected from 15 sites in the western Gulf of Thailand and used for molecular and morphological analyses. One mitochondrial (cox3) and two chloroplast (psbA and rbcL) genes were amplified and sequenced for molecular phylogenetic analyses. Based primarily on phylogenetic evidence, two new species were formally described, L. chumphonensis sp. nov. and L. thailandensis sp. nov. Additionally, L. lamourouxii was newly recorded from Thailand. Two new lineages of Lobophora obscura were identified, L. obscura12 and L. obscura13. Among the Lobophora species identified, three were found in interaction with corals, the most notable of which was the massive coral Porites. Lobophora chumphonensis sp. nov. only interacted with Porites by growing on bare coral skeleton between Porites colonies. Furthermore, L. obscura13 was observed under the branching coral Pocillopora. Our findings revealed that Lobophora presented both effects and absence of effects on coral. A thorough understanding of Lobophora diversity and ecology is essential for ongoing and future research on coral-macroalgal ecological relationships.

Kluyveromyces marxianus: a potential biocatalyst of renewable chemicals and lignocellulosic ethanol production.

Kluyveromyces marxianus is an ascomycetous yeast which has shown promising results in cellulosic ethanol and renewable chemicals production. It can survive on a variety of carbon sources under industrially favorable conditions due to its fast growth rate, thermotolerance, and acid tolerance. K. marxianus, is generally regarded as a safe (GRAS) microorganism, is widely recognized as a powerhouse for the production of heterologous proteins and is accepted by the US Food and Drug Administration (USFDA) for its pharmaceutical and food applications. Since lignocellulosic hydrolysates are comprised of diverse monomeric sugars, oligosaccharides and potential metabolism inhibiting compounds, this microorganism can play a pivotal role as it can grow on lignocellulosic hydrolysates coping with vegetal cell wall derived inhibitors. Furthermore, advancements in synthetic biology, for example CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats with Cas9)-mediated genome editing, will enable development of an engineered yeast for the production of biochemicals and biopharmaceuticals having a myriad of industrial applications. Genetic engineering companies such as Cargill, Ginkgo Bioworks, DuPont, Global Yeast, Genomatica, and several others are actively working to develop designer yeasts. Given the important traits and properties of K. marxianus, these companies may find it to be a suitable biocatalyst for renewable chemicals and fuel production on the large scale. This paper reviews the recent progress made with K. marxianus biotechnology for sustainable production of ethanol, and other products utilizing lignocellulosic sugars.

The Exact VC Dimension of the WiSARD n -Tuple Classifier.

The Wilkie, Stonham, and Aleksander recognition device (WiSARD) n -tuple classifier is a multiclass weightless neural network capable of learning a given pattern in a single step. Its architecture is determined by the number of classes it should discriminate. A target class is represented by a structure called a discriminator, which is composed of N RAM nodes, each of them addressed by an n -tuple. Previous studies were carried out in order to mitigate an important problem of the WiSARD n -tuple classifier: having its RAM nodes saturated when trained by a large data set. Finding the VC dimension of the WiSARD n -tuple classifier was one of those studies. Although no exact value was found, tight bounds were discovered. Later, the bleaching technique was proposed as a means to avoid saturation. Recent empirical results with the bleaching extension showed that the WiSARD n -tuple classifier can achieve high accuracies with low variance in a great range of tasks. Theoretical studies had not been conducted with that extension previously. This work presents the exact VC dimension of the basic two-class WiSARD n -tuple classifier, which is linearly proportional to the number of RAM nodes belonging to a discriminator, and exponentially to their addressing tuple length, precisely N(2n-1)+1 . The exact VC dimension of the bleaching extension to the WiSARD n -tuple classifier, whose value is the same as that of the basic model, is also produced. Such a result confirms that the bleaching technique is indeed an enhancement to the basic WiSARD n -tuple classifier as it does no harm to the generalization capability of the original paradigm.

Biodegradable alternative for removing toxic compounds from sugarcane bagasse hemicellulosic hydrolysates for valorization in biorefineries.

Among the major challenges for hemicellulosic hydrolysate application in fermentative processes, there is the presence of toxic compounds generated during the pretreatment of the biomass, which can inhibit microbial growth. Therefore, the development of efficient, biodegradable and cost-effective detoxification methods for lignocellulosic hydrolysates is crucial. In this work, two tannin-based biopolymers (called A and B) were tested in the detoxification of sugarcane bagasse hydrolysate for subsequent fermentation by Candida guilliermondii. The effects of biopolymer concentration, pH, temperature, and contact time were studied using a 24 experimental design for both biopolymers. Results revealed that the biopolymer concentration and the pH were the most significant factors in the detoxification step. Biopolymer A removed phenolics, 5-hydroxymethylfurfural, and nickel from the hydrolysate more efficiently than biopolymer B, while biopolymer B was efficient to remove chromium at 15% (v/v). Detoxification enhanced the fermentation of sugarcane bagasse hydrolysate, and the biopolymers showed different influences on the process.

A universal multilingual weightless neural network tagger via quantitative linguistics.

In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided.

Biochemical conversion of sugarcane straw hemicellulosic hydrolyzate supplemented with co-substrates for xylitol production.

Biotechnological production of xylitol is an attractive route to add value to a sugarcane biorefinery, through utilization of the hemicellulosic fraction of sugarcane straw, whose availability is increasing in Brazil. Herein, supplementation of the sugarcane straw hemicellulosic hydrolyzate (xylose 57gL(-1)) with maltose, sucrose, cellobiose or glycerol was proposed, and their effect as co-substrates on xylitol production by Candida guilliermondii FTI 20037 was studied. Sucrose (10gL(-1)) and glycerol (0.7gL(-1)) supplementation led to significant increase of 8.88% and 6.86% on xylose uptake rate (1.11gL(-1)h(-1) and 1.09gL(-1)), respectively, but only with sucrose, significant increments of 12.88% and 8.69% on final xylitol concentration (36.11gL(-1)) and volumetric productivity (0.75gL(-1)h(-1)), respectively, were achieved. Based on these results, utilization of complex sources of sucrose, derived from agro-industries, as nutritional supplementation for xylitol production can be proposed as a strategy for improving the yeast performance and reducing the cost of this bioprocess by replacing more expensive nutrients.

Multilingual part-of-speech tagging with weightless neural networks.

Training part-of-speech taggers (POS-taggers) requires iterative time-consuming convergence-dependable steps, which involve either expectation maximization or weight balancing processes, depending on whether the tagger uses stochastic or neural approaches, respectively. Due to the complexity of these steps, multilingual part-of-speech tagging can be an intractable task, where as the number of languages increases so does the time demanded by these steps. WiSARD (Wilkie, Stonham and Aleksander's Recognition Device), a weightless artificial neural network architecture that proved to be both robust and efficient in classification tasks, has been previously used in order to turn the training phase faster. WiSARD is a RAM-based system that requires only one memory writing operation to train each sentence. Additionally, the mechanism is capable of learning new tagged sentences during the classification phase, on an incremental basis. Nevertheless, parameters such as RAM size, context window, and probability bit mapping, make the multilingual part-of-speech tagging task hard. This article proposes mWANN-Tagger (multilingual Weightless Artificial Neural Network tagger), a WiSARD POS-tagger. This tagger is proposed due to its one-pass learning capability. It allows language-specific parameter configurations to be thoroughly searched in quite an agile fashion. Experimental evaluation indicates that mWANN-Tagger either outperforms or matches state-of-art methods in accuracy with very low standard deviation, i.e., lower than 0.25%. Experimental results also suggest that the vast majority of the languages can benefit from this architecture.

Early detection of epilepsy seizures based on a weightless neural network.

This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.

Taxonomic assessment and enzymes production by yeasts isolated from marine and terrestrial Antarctic samples.

The aim of the present study was to investigate the taxonomic identity of yeasts isolated from the Antarctic continent and to evaluate their ability to produce enzymes (lipase, protease and xylanase) at low and moderate temperatures. A total of 97 yeast strains were recovered from marine and terrestrial samples collected in the Antarctica. The highest amount of yeast strains was obtained from marine sediments, followed by lichens, ornithogenic soils, sea stars, Salpa sp., algae, sea urchin, sea squirt, stone with lichens, Nacella concinna, sea sponge, sea isopod and sea snail. Data from polyphasic taxonomy revealed the presence of 21 yeast species, distributed in the phylum Ascomycota (n = 8) and Basidiomycota (n = 13). Representatives of encapsulated yeasts, belonging to genera Rhodotorula and Cryptococcus were recovered from 7 different Antarctic samples. Moreover, Candida glaebosa, Cryptococcus victoriae, Meyerozyma (Pichia) guilliermondii, Rhodotorula mucilaginosa and R. laryngis were the most abundant yeast species recovered. This is the first report of the occurrence of some species of yeasts recovered from Antarctic marine invertebrates. Additionally, results from enzymes production at low/moderate temperatures revealed that the Antarctic environment contains metabolically diverse cultivable yeasts, which could be considered as a target for biotechnological applications. Among the evaluated yeasts in the present study 46.39, 37.11 and 14.43 % were able to produce lipase (at 15 °C), xylanase (at 15 °C) and protease (at 25 °C), respectively. The majority of lipolytic, proteolytic and xylanolytic strains were distributed in the phylum Basidiomycota and were mainly recovered from sea stars, lichens, sea urchin and marine sediments.

Effectively addressing complex proteomic search spaces with peptide spectrum matching.

SUMMARY: Protein identification by mass spectrometry is commonly accomplished using a peptide sequence matching search algorithm, whose sensitivity varies inversely with the size of the sequence database and the number of post-translational modifications considered. We present the Spectrum Identification Machine, a peptide sequence matching tool that capitalizes on the high-intensity b1-fragment ion of tandem mass spectra of peptides coupled in solution with phenylisotiocyanate to confidently sequence the first amino acid and ultimately reduce the search space. We demonstrate that in complex search spaces, a gain of some 120% in sensitivity can be achieved. AVAILABILITY: All data generated and the software are freely available for academic use at http://proteomics.fiocruz.br/software/sim. CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of sorghum straw hemicellulosic hydrolysate for biotechnological production of xylitol by Candida guilliermondii

A preliminary study on xylitol production by Candida guilliermondii in sorghum straw hemicellulosic hydrolysate was performed. Hydrolysate had high xylose content and inhibitors concentrations did not exceed the commonly found values in other hemicellulosic hydrolysates. The highest xylitol yield (0.44 g/g) and productivity (0.19 g/Lh) were verified after 72 hours.

Can the false-discovery rate be misleading?

The decoy-database approach is currently the gold standard for assessing the confidence of identifications in shotgun proteomic experiments. Here, we demonstrate that what might appear to be a good result under the decoy-database approach for a given false-discovery rate could be, in fact, the product of overfitting. This problem has been overlooked until now and could lead to obtaining boosted identification numbers whose reliability does not correspond to the expected false-discovery rate. To overcome this, we are introducing a modified version of the method, termed a semi-labeled decoy approach, which enables the statistical determination of an overfitted result.

Evaluation of sorghum straw hemicellulosic hydrolysate for biotechnological production of xylitol by Candida guilliermondii.

A preliminary study on xylitol production by Candida guilliermondii in sorghum straw hemicellulosic hydrolysate was performed. Hydrolysate had high xylose content and inhibitors concentrations did not exceed the commonly found values in other hemicellulosic hydrolysates. The highest xylitol yield (0.44 g/g) and productivity (0.19 g/Lh) were verified after 72 hours.

Batch xylitol production by Candida guilliermondii FTI 20037 from sugarcane bagasse hemicellulosic hydrolyzate at controlled pH values.

Batch fermentation of sugarcane bagasse hemicellulosic hydrolyzate by the yeast Candida guilliermondii FTI 20037 was performed using controlled pH values (3.5, 5.5, 7.5). The maximum values of xylitol volumetric productivity ( Q(p)=0.76 g/l h) and xylose volumetric consumption ( Q(s)=1.19 g/l h) were attained at pH 5.5. At pH 3.5 and 7.5 the Q(p) value decreased by 66 and 72%, respectively. Independently of the pH value, Y(x/s) decreased with the increase in Y(p/s) suggesting that the xylitol bioconversion improves when the cellular growth is limited. At the highest pH value (7.5), the maximum specific xylitol production value was the lowest ( q(pmax)=0.085 g/l h.), indicating that the xylose metabolism of the yeast was diverted from xylitol formation to cell growth.

A generalized locomotion CPG architecture based on oscillatory building blocks.

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Sugarcane bagasse as alternative packing material for biofiltration of benzene polluted gaseous streams: a preliminary study.

Removal of benzene vapor from gaseous streams was studied in two identically sized lab-scale biofiltration columns: one filled with a mixture of raw sugarcane bagasse and glass beads, and the other one packed with a mixture of ground sugarcane bagasse and glass beads, in the same volume ratio, as filter materials. Separate series of continuous tests were performed, in parallel, under the same operating conditions (inlet benzene concentration of 10.0, 20.0 or 50.0 mg m(-3), and superficial gas velocity of 30.6, 61.2 or 122.4 m h(-1)) in order to evaluate and compare the influence of the packing material characteristics upon the biofilter effectiveness. The maximum elimination capacities obtained, at an inlet load of 6.12 g m(-3) h(-1), were 3.50 and 3.80 g m(-3)packibng material h(-1) with raw and ground sugarcane bagasse, respectively. This was a preliminary study and the results obtained suggest only a limited application with more work needed.

Application of factorial design to the study of xylitol production from eucalyptus hemicellulosic hydrolysate.

This study deals with the bioconversion of xylose into xylitol by Candida guilliermondii FTI 20037 using eucalyptus hemicellulosic hydrolysate obtained by acid hydrolysis. The influence of various parameters (ammonium sulfate, rice bran, pH, and xylose concentration) on the production of xylitol was evaluated. The experiments were based on multivariate statistical concepts, with the application of factorial design techniques to identify the most important variables in the process. The levels of these variables were quantified by the response surface methodology, which permitted the establishment of a significant mathematical model with a coefficient determination of R2 = 0.92. The best results (xylitol = 10.0 g/L, yield factor = 0.2 g/g, and productivity = 0.1 g/[L x h]) were attained with hydrolysate containing ammonium sulfate (1.1 g/L), rice bran (5.0 g/L), and xylose (initial concentration of 60.0 g/L), after 72 h of fermentation. The pH of fermentation was adjusted to 8.0 and the inoculum level utilized was 3 g/L.

Metabolic study of the adaptation of the yeast Candida guilliermondii to sugarcane bagasse hydrolysate.

Batch xylitol production from concentrated sugarcane bagasse hydrolysate by Candida guilliermondii was performed by progressively adapting the cells to the medium. Samples were analyzed to monitor sugar and acetic acid consumption, xylitol, arabitol, ethanol, and carbon dioxide production, as well as cell growth. Both xylitol yield and volumetric productivity remarkably increased with the number of adaptations, demonstrating that the more adapted the cells, the better the capacity of the yeast to reduce xylose to xylitol in hemicellulose hydrolysates. Substrate and product concentrations were used in carbon material balances to study in which way the different carbon sources were utilized by this yeast under microaerobic conditions, as well as to shed light on the effect of the progressive adaptation to the medium on its fermentative activity. Such a theoretical means allowed estimation for the first time of the relative contribution of each medium component to the formation of the main products of this fermentation system.