The effect of vitamin E on mast cells in small intestine of broilers under heat stress

ΔΕΝ ΥΠΑΡΧΕΙ ΕΛΛΗΝΙΚΗ ΠΕΡΙΛΗΨΗThe aim of this study is to identify the effect of vitamin E (DL-α-tocopherol acetate) (300 IU/kg) on mast cells in the small intestine (duodenum, jejunum and ileum) under heat stress. In the study, 42 one-day-old Ross 308 male broiler chicks were used. The chicks were randomly separated into 3 groups as follows; control (22±2°C), heat stress (35°C, 5 hours/per day) and vitamin E (300 IU/kg/per day) + heat stress (35°C, 5 hours/per day). The applications of heat stress and vitamin E began on the fifteenth day and ended on the thirty-fifth day. Tissue samples were taken from animals in each group of four and five-week-old chickens. Tissue samples were fixed in BLA (Basic Lead Acetate) solution. The sections were stained with toluidine blue (TB) (pH 0.5) and alcian blue-critical electrolyte concentration (AB-CEC) (pH 5.8, 0.3 M MgCl2) / Safranin O (SO) (pH 1.0) combined method. It was determined that increasing of the exposure duration to heat stress increased the number of mast cells in the small intestine of the boilers. Also, it was revealed that vitamin E reduced mast cell population under heat stress. Consequently, heat stress may play a role in the pathogenesis of small intestine-associated with disorders and the supplementation of vitamin E can contribute to regulate small intestine functions of broilers by decreasing mast cell proliferation and activation under heat stress

Mass distributions for nuclear disintegration from fission to evaporation

By a proper choice of the excitation energy per nucleon we analyze the mass distributions of the nuclear fragmentation at various excitation energies. Starting from low energies (between 0.1 and 1 MeV/nucleon) up to higher energies about 12 MeV/n, we classified the mass yield characteristics for heavy nuclei (A>200) on the basis of Statistical Multifragmentation Model. The evaluation of fragment distribution with the excitation energy show that the present results exhibit the same trend as the experimental ones.Comment: 5 pages, 3 figure

Production of microporous Cu-doped BTC (Cu-BTC) metal-organic framework composite materials, superior adsorbents for the removal of methylene blue (Basic Blue 9)

Cellulosic woven waste was used as a biomass material to prepare a Cu-doped BTC (Cu-BTC) adsorbent, which was then used to remove methylene blue (Basic Blue 9) from wastewater. Cellulosic woven waste was used as a biomass material to prepare a Cu-doped BTC (Cu-BTC) adsorbent, which was then used to remove methylene blue (Basic Blue 9) from wastewater. The Cu-BTC had higher adsorption capacity for methylene blue (BB9) than pure woven waste because it had high specific surface area and electrostatic interaction with cationic methylene blue molecules. The Cu-BTC removed methylene blue from wastewater rapidly and effectively and had an excellent adsorption capacity (197.90 mg/g). In batch process, the adsorption efficiency of the adsorbent for removal of BB9 was evaluated within 20 degrees C-60 degrees C, with initial BB9 concentrations of 50 - 200 mg/L and initial pH of 2 -11. The Cu-BTC activation tailored the topological and textural properties of the obtained adsorbent, leading to a relatively large surface area of 1418.3 m(2)/g and pores with a volume of 0.491 cm(3)/g and an average size of 2.11 nm. The adsorption process fitted well with the Langmuir isotherm and the pseudo-second-order kinetic model. The possible mechanism for methylene blue removal mainly involved electrostatic attraction and micro pores. This study can serve as a guide for value-added utilization of cellulosic woven waste and as a practical method for the removal of methylene blue from wastewater. Adsorption of methylene blue onto the CuBTC is an effective and eco-friendly method for its removal from wastewater

Effect of Dietary Oregano and Rosemary Essential Oil Supplementation on Growth Performance and Cecal Microbiota of Broilers

In this study, the effect of dietary supplementation of oregano and rosemary essential oils (EO) on growth performance and cecal microbiota of broilers were investigated. A total of 450 1-d-old male Ross-308 broilers were divided into 5-experimental groups (10 replicates of 9 chickens): a Control (C), fed a basal diet; four treatments, which received a basal diet supplemented with oregano and rosemary EOs individually (O, 300 mg/kg oregano EO; R, 300 mg/kg rosemary EO) and combined (OR1, 150 mg/kg oregano EO + 150 mg/kg rosemary EO; OR2, 200 mg/kg oregano EO + 200 mg/kg rosemary EO). Body weight (BW), feed intake (FI), body weight gain (BWG), feed conver-sion ratio (FCR), and cecal microbiota (coliforms, clostridia and lactobacilli) were determined weekly, and at 42 d, re-spectively. BW in R (p < 0.05) and OR2 (p < 0.001), and BWG and FCR in OR2 (p < 0.05) were significantly higher than C at 42 d, despite no difference in FI in any group during experimental period. Counts of cecal coliforms (p < 0.001) and clostridia (p < 0.01) decreased, and lactobacilli (p < 0.001) increased substantially between C and treatment groups. Results indicated that combined oregano and rosemary EO (200 mg/kg ea) supplementation significantly increased BW and BWG, improved FCR in 1-42 d, lowered coliform and clostridial, and increased lactobacilli counts suggesting a beneficial shift in cecal microbiota.Bursa Uludag Uni-versity Scientific Research Unit Grant [HDP (V) -2014/45]ACKNOWLEDGEMENT This study was funded by the Bursa Uludag Uni-versity Scientific Research Unit Grant, Project No: HDP (V) -2014/45

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem. The majority of current ML approaches ignore real-world challenges such as the detection of novel malware. In addition, proposed ML approaches are often designed either for malware/benign-ware classification or malware family classification. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.Comment: Accepted at IEEE ISI 202

Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of realistic evaluation factors. These factors include learning under class imbalance, the ability to identify new malware, and the cost of production-quality labeled data. In practice, deployed models face prominent, rare, and new malware families. At the same time, obtaining a large quantity of up-to-date labeled malware for training a model can be expensive. In this paper, we address these problems and propose a novel hierarchical semi-supervised algorithm, which we call the HNMFk Classifier, that can be used in the early stages of the malware family labeling process. Our method is based on non-negative matrix factorization with automatic model selection, that is, with an estimation of the number of clusters. With HNMFk Classifier, we exploit the hierarchical structure of the malware data together with a semi-supervised setup, which enables us to classify malware families under conditions of extreme class imbalance. Our solution can perform abstaining predictions, or rejection option, which yields promising results in the identification of novel malware families and helps with maintaining the performance of the model when a low quantity of labeled data is used. We perform bulk classification of nearly 2,900 both rare and prominent malware families, through static analysis, using nearly 388,000 samples from the EMBER-2018 corpus. In our experiments, we surpass both supervised and semi-supervised baseline models with an F1 score of 0.80.Comment: Accepted at ACM TOP