Miracle-dz/UniFAR-FTUs huggingface dataset:
Aeromonas hydrophila in Nile tilapia (Oreochromis niloticus) from Brazilian aquaculture: a public health problem

Aeromonas hydrophila is a Gram-negative bacterium present in the water, which can cause disease in animals, such as fish, frog, and mammals, including humans. In fish, Aeromonosis occurs when it is immunosuppressed due to the stress of handling, water quality, parasitism or population density. Due the importance of this disease in fish and humans, this study aimed to detect this bacterium by PCR in Nile tilapia (Oreochromis niloticus) of cage fish farms localized in hydro-electrical reservoirs of São Paulo state, Brazil and describe the lesions found in positive fish by necropsy and histopathology. Around 360 samples of Oreochromis niloticus specimens were randomly sampled at six Brazilian fish farms in November 2014 (n = 180) and in March 2015 (n = 180). The identification of A. hydrophila by PCR showed the prevalence since 3.33% to 46.66%. The most common macroscopic lesions were hemorrhage and splenomegaly, and bacteria colonies, coagulative necrosis, hemorrhage, inflammatory process, melano-macrophages, and vacuolar degeneration were microscopic. The pathological and histopathological findings showed the presence of an infectious disease, and employing the molecular technique, it was possible to identify that the analyzed fishes had A. hydrophila. Thus, producers should utilize this information using histopathology and molecular techniques in tilapia to reduce economic losses and avoid disease in consumers.
Stable feature selection: theory and algorithms

Feature selection plays an important role in knowledge discovery from many application domains with high-dimensional data. Many feature selection algorithms have been developed and shown successful at improving predictive accuracy of learning models while reducing feature space dimensionality and model complexity. Besides high accuracy, the stability of feature selection - the insensitivity of the result of a feature selection algorithm to variations to the training set, is another important yet under-addressed issue for feature selection. The stability issue has become increasingly critical in application domains where feature selection is used as a knowledge discovery tool to identify important features for explaining the observed phenomena, such as biomarker identification in cancer diagnosis. In this dissertation, we present a theoretical framework about the relationship between the stability and the accuracy of feature selection based on a formal biasvariance decomposition of feature selection error. The framework also reveals the connection between stability and sample size and suggests a variance reduction approach for improving the stability of feature selection algorithms under small sample size. Following the theoretical framework, we also develop an empirical variance reduction framework and margin based instance weighting algorithms under this framework. Moreover, our extensive experimental study verifies the theoretical framework and the empirical framework based on both synthetic data sets and real-world microarray data sets. Our results show that the empirical framework is effective at reducing the variance and improving the stability of two representative feature selection algorithms, SVM-RFE and ReliefF, while maintaining comparable predictive accuracy based on the selected features. The proposed instance weighting framework is also shown to be more effective and efficient than the existing ensemble framework at improving the subset stability of the feature selection algorithms under study.
SURF and KPCA based image perceptual hashing algorithm

Image perceptual hashing is a notable concept in the field of image processing. Its application ranges from image retrieval, image authentication, image recognition, to content-based image management. In this paper a novel image hashing algorithm based on SURF and KPCA, which extracts speed-up robust feature as the perceptual feature, is proposed. SURF retains the robust properties of SIFT, and it is 3 to 10 times faster than SIFT. Then, the Kernel PCA is used to decompose key points’ descriptors and get compact expressions with well-preserved feature information. To improve the precision of digest matching, a binary image template of input image is generated which contains information of salient region to ensure the key points in it have greater weight during matching. After that, the hashing digest for image retrieval and image recognition is constructed. Experiments indicated that compared to SIFT and PCA based perceptual hashing, the proposed method could increase the precision of recognition, enhance robustness, and effectively reduce process time.
Consistency Analysis for with Changing MAC Parameters in Wireless Ad hoc Networks

A wireless ad hoc network represents a system of wireless mobile nodes, which can freely and dynamically self-organize into arbitrary and temporary network topologies allowing people and devices to connect in the area without any pre-existing communication infrastructure. Devices in wireless ad hoc network should be able to detect the presence of other devices and perform necessary setup to facilitate communication and sharing of data and service. This paper presents consistency of routing protocols AODV and DSR by incorporating changes in performance parameters of the network design. The performance has been validated using QualNet 5.2 simulation software and results are analysed according to the performance requirements of the network.
Effects of simulated acid rain on soil respiration rate and soil bacterial diversity in a Phyllostachyspubescens forest in subtropical China

Acid rain has been regarded as a global environmental concern due to its negative effects on global ecosystems. In this study, we investigated the effects of simulated acid rain (SAR) on soil respiration rate and soil bacterial diversity in a Moso bamboo (phyllostachyspubescens) forest in subtropical China. Experimental results showed a similar seasonal pattern of soil respiration rates underdifferent SAR treatments. Seasonal mean soil respiration rates for CK (control, deionized water, pH 6.7), T1 (pH 5.6), T2 (pH 4.0) and T3 (pH 2.5) treatments were 3.44, 4.80, 4.35 and 4.51 μ mol m−2 s−1, respectively. One-way analysis of variance indicated that the SAR exposure had no significant effect on soil respiration (p>0.1) and soil microbial biomass (p>0.1). Soil bacterial community diversity was calculated as the Shannon-Wiener diversity index and the results showed that only T3 treatment had significant effects on soil bacterial diversity. The DGGE analysis results revealed that T1 and CK soils had closer association and were related to the T2 soil, while T3 soil was distinctly different from the other treatments. This work highlights that the effects of SAR are important to consider in assessing the soil respiration rate, particularly under the scenario of increasing acid rain pollution.
Data augmentation in Riemannian space for Brain-Computer Interfaces

Brain-Computer Interfaces (BCI) try to interpret brain signals , such as EEG , to issue some command or to characterize the cognitive states of the subjects. A strong limitation is that BCI tasks require a high concentration of the user , de facto limiting the length of experiment and the size of the dataset. Furthermore , several BCI paradigms depend on rare events , as for event-related potentials , also reducing the number of training examples available. A common strategy in machine learning when dealing with scarce data is called data augmentation ; new samples are generated by applying chosen transformations on the original dataset. In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and a simple multi-layer perceptron offers good results . Experimental validation is conducted on two datasets : an SSVEP experiment with few training samples in each class and an error potential experiment with unbalanced classes (NER Kaggle competition) .
Processing and Visualization of Metabolomics Data Using R

Metabolomics provides a wealth of information about the biochemical status of cells, tissues, and other biological systems. However, for many researchers, processing the large quantities of data generated in typical metabolomics experiments poses a formidable challenge. Robust computational tools are required for all data processing steps, from handling raw data to high level statistical analysis and interpretation. This chapter describes several established methods for processing and analyzing metabolo‐ mics data within the R statistical programming environment. The focus is on processing LCMS data but the methods can be applied virtually to any analytical platform. We provide a step‐by‐step workflow to demonstrate how to integrate, analyze, and visualize LCMS‐based metabolomics data using computational tools available in R. These concepts and methods will allow specialists and nonspecialists alike to develop and evaluate their own data more critically.
Advancing feature selection research

The rapid advance of computer based high-throughput technique have provided unparalleled opportunities for humans to expand capabilities in production, services, communications, and research. Meanwhile, immense quantities of high-dimensional data are accumulated challenging state-of-the-art data mining techniques. Feature selection is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing the irrelevant (and the redundant) features. In the past few .
Incidence and Fusion of Os Trigonum in a Healthy Pediatric Population

Background: Accessory ossicles off the talus (os trigonum) are a common radiographic finding seen within the foot with variable symptomatology in adult and pediatric patients. Although most studies have examined the incidence of these anatomic variants in a retrospective manner, few have longitudinally followed the behavior of os trigonum over time. The aim of this study was to investigate the incidence, mean age of appearance and fusion, and fusion rate of os trigonum in a large cohort of asymptomatic pediatric patients. Methods: A total of 2620 serial digitized unilateral foot and ankle radiographs from 261 healthy white children (age range, 0.25 to 7 y at enrollment) were retrospectively reviewed. Radiographs were examined to determine the incidence of os trigonum, noting age of appearance and if present, the age of fusion. Skeletal maturity was graded based on ossification pattern of the calcaneal apophysis. Results: Radiographic evidence of os trigonum was present in 18% of patients (n=16 females, n=15 males) with radiographs extending 1 SD beyond the average age of os trigonum appearance. There was no difference in incidence between females and males (P=0.27), and os trigonum was found to appear at a significantly younger age in females (9.1±1.8 y) than males (10.4±2.0 y, P=0.04). Overall, 70% (n=26) of subjects with os trigonum achieved fusion by age 17 (mean, 9.5±1.6 y in females, 11.2±1.6 in males) with no significant difference between sexes (P=0.61). Grading of skeletal maturity at the time of fusion showed that both male and female subjects underwent fusion on average between calcaneal stages 2 and 3 (P=0.5). Conclusions: This study confirms a relatively large prevalence of os trigonum in a random pediatric population, and clarifies both the average age of onset and high rate of fusion at a relatively narrow range of skeletal maturity. Level of Evidence: Prognostic level II—retrospective study.
Reacción en cadena de la polimerasa (RCP) como herramienta diagnóstica de leucosis enzoótica bovina

Se analiza la posibilidad de utilizacion de RCP como herramienta diagnostica de Leucosis Enzootica Bovina, comparandola con las tecnicas serologicas tradicionales como inmunodifusion en gel de agar (IDGA) e inmunoensayo enzimatico (ELISA), en las condicones epidemiologicas presentes en Argentina con respecto a dicha enfermedad. Se discuten ademas las alternativas de distintas estrategias asociadas a RCP, de tal manera de que la misma constituya una tecnica de uso rutinario en laboratos de mediana complejidad. Se concluye que esta tecnica adquiere importancia para condiciones de prevalencias muy bajas, en casos puntuales, donde sea necesario definir situaciones epidemiologicas especificas.
Monosynaptic Projections to Excitatory and Inhibitory preBötzinger Complex Neurons

The key driver of breathing rhythm is the preBötzinger Complex (preBötC) whose activity is modulated by various categorical inputs, e.g., volitional, physiological, emotional. While the preBötC is highly interconnected with other regions of the breathing central pattern generator (bCPG) in the brainstem, there is no data about the direct projections to either excitatory and inhibitory preBötC subpopulations from other elements of the bCPG or from suprapontine regions. Using modified rabies tracing, we identified neurons throughout the brain that send monosynaptic projections to identified excitatory and inhibitory preBötC neurons. Within the brainstem, neurons from sites in the bCPG, including the contralateral preBötC, Bötzinger Complex (BötC), the nucleus of the solitary tract (NTS), parafacial region (pFL/pFV or RTN/pFRG), and parabrachial nuclei, send direct projections to both excitatory and inhibitory preBötC neurons. Suprapontine inputs to the excitatory and inhibitory preBötC neurons include the superior colliculus, red nucleus, amygdala, hypothalamus, and cortex; these projections represent potential direct pathways for volitional, emotional, and physiological control of breathing.
Modelling, analysis, and simulation of mobile-to-mobile MIMO channels

The development of mobile communication has been dramatically increasing in recent years. New concepts and methods are necessary for the improvement of existing mobile communication techniques due to the fact of the demand for these systems with higher data rates and a better quality of service. As the number of subscribers is growing rapidly, the development of mobile radio channel models has become one of the most important research topics within mobile communication systems. This thesis deals with a multiple-input multiple-output (MIMO) channel model for mobileto-mobile communications. It starts with an analysis of a single-input single-output (SISO) channel model which is based on a geometrical street model under the assumption that the transmitter and the receiver are moving. Starting from the geometrical model, statistical properties of the analytical and simulation models will be studied. Of special interest are the probability density function, temporal autocorrelation function, Doppler power spectral density, level-crossing rate, and average duration of fades. Furthermore, the analytical results will be verified by MATLAB simulations. In the last part of this thesis the extensions of the SISO street model with respect to multiple clusters of scatterers, frequency selectivity, and to a MIMO street model are considered.
Comparison of antioxidant activity, Phenolic and Flavonoid contents of selected medicinal plants in Sri Lanka

Oxidative stress related diseases are as a result of accumulation of free radicals in the cellular organs. Plant based antioxidants play a defensive role by preventing the generation of free radicals and therefore the main focus of this study was to screen twenty five Sri Lankan medicinal plants for comparison of antioxidant capacity. Aqueous extracts of twenty five plants were screened for antioxidant activity by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Six plants that gave high antioxidant activity Terminalia chebula Retz. , Punica granatum L. , Flueggea leucopyrus Willd. , Cassia fistula L. , Piper betle L. and Phyllanthus amarus were selected for further analysis. Methanol extracts of the selected plants were subjected to assess IC50 by DPPH assay. Total phenolic and flavonoid contents were analyzed by Folin Ceocalteu method and Aluminium Colorimetric method respectively. Statistical analysis was done by Minitab 17 package using regression analysis techniques to identify the effect of IC50 by phenolics and flavonoids. Seeds of T. chebula Retz. showed high antioxidant activity with 102 mg/ml of IC50 value. Antioxidant activity of rest of the plants in the descending order was F. leucopyrus Willd. (twigs) > C. fistula L. (bark) > P.granatum L. (leaves) > P. betle L. (leaves) >P. amarus (twigs). The relationship between IC50 by phenolic and flavonoid content was statistically significant. Further IC50 has a strong negative relationship between phenolics and flavonoids. Data from present study revealed that Terminalia chebula Retz. , Punica granatum L. , Flueggea leucopyrus Willd. , Cassia fistula L. , Piper betle L. and Phyllanthus amarus possess high antioxidant capacity compared to other medicinal plants.
Enlarging the Scope of Randomization and Permutation Tests in Neuroimaging and Neuroscience

The statistical analysis of biological data is mostly performed in a parametric framework. One of the problems with this approach is that the false alarm (FA) rate of these parametric statistical tests (the probability of falsely rejecting the null hypothesis) often cannot be controlled (Eklund et al., 2016). Besides other advantages (higher sensitivity), nonparametric tests are claimed to provide an exact control of the FA rate (Maris and Oostenveld, 2007; Nichols and Holmes, 2002), and they are claimed to do so independently of a number of parameters that affect the FA rate of parametric statistical tests (sample size, the clustering threshold in case cluster-based test statistics are used, the spatial and temporal correlation, etc.). However, there are study types for which some nonparametric tests do not provide an exact control of the FA rate, such as studies in which the explanatory variable is manipulated within participants (Eklund et al., 2016). In the present paper, we present a family of nonparametric randomization and permutation tests that provide an exact control of the FA rate, and do so for a much larger family of study types than before. The crucial element of this statistical innovation is the adoption of a novel but highly relevant null hypothesis: statistical independence between the biological and the explanatory variable.
Evaluation of the merits of two drugs-dexmedetomidine and ketamine for day care hysteroscopic procedures

Background: The aim was to compare the hemodynamic changes intra operatively and to ascertain the superior efficacy of the study drugs Dexmedetomidine, a highly selective alpha-2-adrenoceptor agonist having sympatholytic, dose dependent sedation, analgesic properties, amnestic effect without respiratory depression and Ketamine, a n-methyl-d-aspartate receptor antagonist, a dissociative sedative hypnotic with potent analgesic properties and marked sympathomimetic effects, have been taken for day care (ambulatory) hysteroscopic procedures. The objectives are to evaluate the requirement of rescue sedative and /or analgesic during the procedure and any complication of the drugs per-operatively.Methods: About 25 patients in each group in the range of age 20 to 55 years, weight 40-60 kg, height 145-155 cm, ASA I and II were injected with dexmedetomidine (D) 100 µg IV or Ketamine (K) 75 mg IV both over 10 mins at the onset of the procedure. Maintenance of anaesthesia had been done on mask ventilation with 30% oxygen and 70% nitrous oxide.Results: The demographic data was statistically insignificant. The haemodynamics were stable in the group D, not in group K, ‘p’ value <0.0001. The request for rescue sedation propofol and rescue analgesic fentanyl and the number of top up doses were higher in the group K, p’ value <0.01, statistically highly significant. Group K had suffered more per-operative complications.Conclusions: Dexmedetomidine for day care hysteroscopic procedures can be the anaesthetic drug of choice.
A genetic pathway encoding double-stranded RNA transporters and interactors regulates growth and plasticity in Caenorhabditis elegans

The environment and genes shape the development, physiology and behaviour of organisms. Many animal species can take-up double-stranded RNA (dsRNA) from the environment. Environmental dsRNA changes gene expression through RNA interference (RNAi). While environmental RNAi is used as a laboratory tool, e.g. in nematodes, planaria and insects, its biological role remains enigmatic. Here we characterise the environmental dsRNA receptor SID-2 to understand the biological function of dsRNA uptake in Caenorhabditis elegans. First we determine that SID-2 localises to the apical membrane and the trans-Golgi-network (TGN) in the intestine, implicating the TGN as a central cellular compartment for environmental dsRNA uptake. We demonstrate that SID-2 is irrelevant for nucleotide uptake from the environment as a nutritional (nitrogen) source. Instead RNA profiling and high-resolution live imaging revealed a new biological function for sid-2 in growth and phenotypic plasticity. Surprisingly, lack of the ability to uptake environmental RNA reduces plasticity of gene expression. Furthermore, using genetic analyses we show that the dsRNA pathway genes sid-2, sid-1 and rde-4 together regulate growth. This work suggest that environmental RNA affects morphology and plasticity through gene regulation.
The Diaphanous-Related Formins: Dynamic Regulators of Smooth Muscle Cell-Specific Gene Transcription

We have previously shown that smooth muscle cell (SMC) differentiation marker gene expression is regulated by the small GTPase, RhoA. The objective of the present study was to determine the contributions of the RhoA effectors, mouse diaphanous 1 and 2 (mDia1 and mDia2), to this regulatory mechanism. mDia1 and mDia2 are expressed highly in aortic SMC and in a number of SMC-containing organs including bladder, lung, and esophagus. Activation of mDia1/2 signaling by RhoA strongly stimulated SMC-specific promoter activity in multiple cell-types including primary aortic SMC, and stimulated endogenous SM α-actin expression in 10T1/2 cells. Expression of a dominant negative mDia1 variant that
Effects of dietary soybean lecithin oil on the immunoglobulin level and fat globule size of milk in lactating sows

ABSTRACT This study was conducted to investigate the effects of dietary soybean lecithin (SL) on immune function and milk quality in lactating sows. The dietary treatments included a control group and three groups that replace soybean oil with 1%, 2% and 3% SL respectively in the diets. The weight of piglets at weaning and the average daily gain (ADG) were increased by supplementation of 3% SL (P < 0.05). The content of phisphatidylcholine in milk was increased by supplementation of 2% and 3% SL (P < 0.05), and the average diameter of milk fat globule was decreased by supplementation of 1%, 2% and 3% SL (P < 0.05). The immunoglobulin levels of milk and plasma were enhanced when 2% or 3% SL was added (P < 0.05). Collectively, dietary SL decreased the milk fat globule size and improved the content of phospholipid in milk, as well as enhanced the immunoglobulin levels in milk and plasma of sows and piglets.