FastqPuri: high-performance preprocessing of RNA-seq data

Background RNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing. Results We here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness. Conclusions: FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri. It is implemented in C and R and licensed under GPL v3

Generalized multiscale RBF networks and the DCT for breast cancer detection

The use of the multiscale generalized radial basis function (MSRBF) neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work. The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems. In this work instead, MSRBF networks are part of an integrated computer-aided diagnosis (CAD) framework for breast cancer detection, which holds three stages: an input-output model is obtained from the image, followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer. In the first stage, the image data is rendered into a multiple-input-single-output (MISO) system. In order to improve the characterisation, the nonlinear autoregressive with exogenous inputs (NARX) model is introduced to rearrange the available input-output data in a nonlinear way. The forward regression orthogonal least squares (FROLS) algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network. In the second stage, once the network model is available, the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform (DCT). In the third stage, we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection. To test the method performance, three different and well-known public image repositories were used: the mini-MIAS and the MMSD for mammography, and the BreaKHis for histopathology images. A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor. Classification results show that the new CAD method reached an accuracy of 93.5% in mini-Mammo graphic image analysis society (mini-MIAS), 93.99% in digital database for screening mammography (DDSM) and 86.7% in the BreaKHis. We found that the MSRBF networks are able to build tailored and precise image models and, combined with the DCT, to extract high-quality features from both black and white and coloured images

Physicochemical characterization and fatty acid content of ‘venadillo’ (Swietenia humilis Zucc.) seed oil

Physicochemical properties of Swietenia humilis Zucc seed oils were determined along with its fatty acid composition, by using gas-liquid chromatography. The oil content found in the germ portion of the seeds was 45.38%. From physicochemical oil evaluations, an oil density of 0.9099 mg∙ml-1 at 28°C; a refraction index of 1.4740 at 20°C; a saponification index of 159.55 mg KOH∙g-1; a peroxide index of 0.739 meq O2∙kg-1, and 0.367% free fatty acid content were shown. From chromatographic oil evaluations, eight fatty acids were identified showing palmitic (C16:0), stearic (C18:0), oleic (C18:1 cis-9), linoleic (C18:2 cis-9,12), and linolenic (C18:3 cis-9,12,15) as the most predominant. The percentage of saturated, monounsatured and polyunsatured fatty acids were at 18.45, 29.27 and 47.50%, respectively. These results show that ‘venadillo’ oil has a high content of essential fatty acids, mainly linoleic and linolenic. Therefore, this oil shows promissory uses as nutritional component to reduce the cholesterol and triglyceride levels in blood, mostly from patients with higher cardiovascular disease risks.Key words: Oil, α-linolenic acid, linoleic acid, oilseeds, Swietenia humilis

Are congenital malformations more frequent in fetuses with intrahepatic persistent right umbilical vein? A comparative study

Objective Persistent right umbilical vein (PRUV) is a vascular anomaly where the right umbilical vein remains as the only conduit that returns oxygenated blood to the fetus. It has classically been described as associated with numerous defects. We distinguish the intrahepatic variant (better prognosis) and the extrahepatic variant (associated with worse prognosis). The objective of this study was to compare rates of congenital malformations in fetuses with intrahepatic PRUV (I-PRUV) versus singleton pregnancies without risk factors. Materials and Methods A multicenter, crossover design, comparative study was performed between 2003 and 2013 on fetuses diagnosed with I-PRUV (n = 56), and singleton pregnancies without congenital malformation risk factors (n = 4050). Results Fifty-six cases of I-PRUV were diagnosed (incidence 1:770). A statistically significant association between I-PRUV and the presence of congenital malformations (odds ratio 4.321; 95% confidence interval 2.15–8.69) was found. This positive association was only observed with genitourinary malformations (odds ratio 3.038; 95% confidence interval 1.08–8.56). Conclusion Our rate of malformations associated with I-PRUV (17.9%) is similar to previously published rates. I-PRUV has shown a significant increase in the rate of associated malformations, although this association has only been found to be statistically significant in the genitourinary system. Noteworthy is the fact that this comparative study has not pointed to a significant increase in the congenital heart malformation rate. Diagnosis of isolated I-PRUV does not carry a worse prognosis

Spectra of heavy-light and heavy-heavy mesons containing charm quarks, including higher spin states for Nf=2+1N_f=2+ 1

We study the spectra of heavy-light and heavy-heavy mesons containing charm quarks, including higher spin states. We use two sets of $N_f = 2 + 1$ gauge configurations, one set from QCDSF using the SLiNC action, and the other configurations from the Budapest-Marseille-Wuppertal collaboration, using the HEX smeared clover action. To extract information about the excited states, we choose a suitable basis of operators to implement the variational method.Comment: 7 pages, 5 figures, Talk presented at the XXIX International Symposium on Lattice Field Theory, Lattice2011, July 11-16, 2011, The Village at Squaw Valley, California, US

A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

In this paper, a novel Multilayer Interval Type-2 Fuzzy Extreme Learning Machine (ML-IT2-FELM) for the recognition of walking activities and Gait events is presented. The ML-IT2-FELM uses a hierarchical learning scheme that consists of multiple layers of IT2 Fuzzy Autoencoders (FAEs), followed by a final classification layer based on an IT2-FELM architecture. The core building block in the ML-IT2-FELM is the IT2-FELM, which is a generalised model of the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) and that is functionally equivalent to a class of simplified IT2 Fuzzy Logic Systems (FLSs). Each FAE in the ML-IT2-FELM employs an output layer with a direct-defuzzification process based on the Nie-Tan algorithm, while the IT2-FELM classifier includes a Karnik-Mendel type-reduction method (KM). Real data was collected using three inertial measurements units attached to the thigh, shank and foot of twelve healthy participants. The validation of the ML-IT2-FELM method is performed with two different experiments. The first experiment involves the recognition of three different walking activities: Level-Ground Walking (LGW), Ramp Ascent (RA) and Ramp Descent (RD). The second experiment consists of the recognition of stance and swing phases during the gait cycle. In addition, to compare the efficiency of the ML-IT2-FELM with other ML fuzzy methodologies, a kernel-based ML-IT2-FELM that is inspired by kernel learning and called KML-IT2-FELM is also implemented. The results from the recognition of walking activities and gait events achieved an average accuracy of 99.98% and 99.84% with a decision time of 290.4ms and 105ms, respectively, by the ML-IT2-FELM, while the KML-IT2-FELM achieved an average accuracy of 99.98% and 99.93% with a decision time of 191.9ms and 94ms. The experiments demonstrate that the ML-IT2-FELM is not only an effective Fuzzy Logic-based approach in the presence of sensor noise, but also a fast extreme learning machine for the recognition of different walking activities

What do we evaluate in sport mindfulness interventions? A systematic review of commonly used questionnaires

Interest of the study: mindfulness is a concept describing the focus on the present moment, intentionally and without judgement. This approach has only recently been applied to sport psychology. Objectives: the aim of the current review is to investigate which indicators and questionnaires are used in mindfulness research in sport, being specifically interested in mindfulness assessment. Methods: PRISMA guidelines for systematic reviews and the recommendations of the Cochrane Collaboration were used. Literature searches were conducted in Psychinfo, PubMed, EMBASE and the Cochrane Library. Results: From 2, 203 records initially retrieved, 17 articles were included. The results show that mindfulness, anxiety and acceptance are the most commonly studied psychological indicators. The Five Facet Mindfulness Questionnaire is the most frequently used mindfulness scale. We also discuss the possibility of using physiological indicators as complementary assessment. Conclusions: It is recommended to specifically adapt some questionnaires, such is already done with the Sport Anxiety Scale or the Mindfulness Inventory for Sport, for their use in sport psychology

Lattice Study of Conformal Behavior in SU(3) Yang-Mills Theories

Using lattice simulations, we study the extent of the conformal window for an SU(3) gauge theory with N_f Dirac fermions in the fundamental representation. We extend our recently reported work, describing the general framework and the lattice simulations in more detail. We find that the theory is conformal in the infrared for N_f = 12, governed by an infrared fixed point, whereas the N_f = 8 theory exhibits confinement and chiral symmetry breaking. We therefore conclude that the low end of the conformal window N_f^c lies in the range 8 <= N_f^c <= 12. We discuss open questions and the potential relevance of the present work to physics beyond the standard model.Comment: 37 pages, 7 figures. v2: assorted minor updates and correction

Oral direct anticoagulants in the treatment of nonvalvular atrial fibrillation. Results of the daily clinical practice.

Atrial fibrillation (AF) is the most common arrhythmia. It leads to significant morbidity and mortality. The new oral anticoagulants (NOAC) represent an improvement compared with standard treatment (vitamin K antagonists (AVK)) in the prevention of thromboembolic complications in patients with non-valvular AF.N