247 research outputs found
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Application of process algebraic verification and reduction techniques to SystemC designs
SystemC is an IEEE standard system-level language used in hardware/software codesign and has been widely adopted in the industry. This paper describes a formal approach to verifying SystemC designs by providing a mapping to the process algebra mCRL2. Our mapping formalizes both the simulation semantics as well as exhaustive state-space exploration of SystemC designs. By exploiting the existing reduction techniques of mCRL2 and also its model-checking tools, we efficiently locate the race conditions in a system and resolve them. A tool is implemented to automatically perform the proposed mapping. This mapping and the implemented tool enabled us to exploit process-algebraic verification techniques to analyze a number of case-studies, including the formal analysis of a single-cycle and a pipelined MIPS processor specified in SystemC.
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
Interprocedural Reachability for Flat Integer Programs
We study programs with integer data, procedure calls and arbitrary call
graphs. We show that, whenever the guards and updates are given by octagonal
relations, the reachability problem along control flow paths within some
language w1* ... wd* over program statements is decidable in Nexptime. To
achieve this upper bound, we combine a program transformation into the same
class of programs but without procedures, with an Np-completeness result for
the reachability problem of procedure-less programs. Besides the program, the
expression w1* ... wd* is also mapped onto an expression of a similar form but
this time over the transformed program statements. Several arguments involving
context-free grammars and their generative process enable us to give tight
bounds on the size of the resulting expression. The currently existing gap
between Np-hard and Nexptime can be closed to Np-complete when a certain
parameter of the analysis is assumed to be constant.Comment: 38 pages, 1 figur
Proving Safety with Trace Automata and Bounded Model Checking
Loop under-approximation is a technique that enriches C programs with
additional branches that represent the effect of a (limited) range of loop
iterations. While this technique can speed up the detection of bugs
significantly, it introduces redundant execution traces which may complicate
the verification of the program. This holds particularly true for verification
tools based on Bounded Model Checking, which incorporate simplistic heuristics
to determine whether all feasible iterations of a loop have been considered.
We present a technique that uses \emph{trace automata} to eliminate redundant
executions after performing loop acceleration. The method reduces the diameter
of the program under analysis, which is in certain cases sufficient to allow a
safety proof using Bounded Model Checking. Our transformation is precise---it
does not introduce false positives, nor does it mask any errors. We have
implemented the analysis as a source-to-source transformation, and present
experimental results showing the applicability of the technique
Chemopreventive effect of quince (Cydonia oblonga Mill.) fruit extract on hepatocellular carcinoma induced by diethylnitrosamine in rats
Introduction: Hepatocellular carcinoma (HCC) or primary liver cancer is one of the most prevalent and deadliest cancers, which has been increasing greatly worldwide. Diethylnitrosamine (DEN) is a well-known environmental toxin and potent hepatocarcinogenic dialkylnitrosoamine present in air, water, and in a number of foodstuffs. In the present study, we evaluated preventive effect of aqueous extract of quince (Cydonia oblonga Mill.) fruit (ACO) against DEN-induced hepatocellular carcinoma (HCC) in rats.
Methods and Results: The model of hepatocellular carcinoma was induced by a single intraperitoneal injection of DEN (200 mg/kg) as an initiator that after two weeks followed by daily oral administration of 2-acetylaminofluorene (30 mg/kg) as a promoter for two weeks. Quince-treated rats were pretreated with ACO intragastrically at three different doses two weeks prior to DEN injection. The marked reduction of serum biomarkers of liver damage and cancer, including alfa-fetoprotein (AFP), gamma glutamyl transpeptidase (GGT), alanine transaminase (ALT), and aspartate transaminase (AST) were observed in ACO supplemented animals as compared with HCC rats at the end of the experiment. Moreover, the quince extract exhibited in vivo antioxidant activity by elevating glutathione (GSH) contents as well as preventing lipid peroxidation in the liver tissues of DEN-treated rats. The relative weight of liver was also reduced in quince-treaded rats as a prognostic marker in HCC.
Conclusions: Our results clearly demonstrated that quince has a chemopreventive effect against HCC in rats and can be proposed as a promising candidate for the prevention of DEN-induced hepatocarcinogenesis.
 
Engineering of pulmonary surfactant corona on inhaled nanoparticles to operate in the lung system
Exposure of inhaled nanoparticles (NPs) to the deep lung tissue results in the adsorption of pulmonary surfactant (PSf) on the surface of NPs and the formation of a biomolecular corona. The adsorption of the peculiar phospholipids (PLs) and surfactant proteins (SPs) provides NPs with a new bio-identity, which likely changes their corresponding interactions with cells and other bio-systems. Exploring the interaction of NPs with the PSf film at the alveolar air-fluid interface can provide valuable insights into the role of biofluids in the cellular uptake of NPs and their nanotoxic effects. Wrapping biomembranes around NPs and the formation of lipoprotein corona regulate viscoelastic changes, NP insertion into the membrane, and cellular uptake of NPs. In this review, a concise overview has been presented on the engineering of PSf on inhaled NPs to operate in lung environment. First, the physiological barriers in the pulmonary delivery of NPs and approaches to regulating their pulmonary fate are introduced and rationalized. Next, a short description is given on the different sources used for exploring the interfacial performance of inhaled NPs in vitro. A discussion is then presented on SP corona formation on the surface of inhaled NPs, coronal proteome/lipidome in respiratory tract lining fluid (RTLF), regulation of NP aggregation and surfactant flow characteristics, PSf corona and its functional role in the cellular uptake of NPs, followed by explanations on the clinical correlations of PSf corona formation/inhibition on the surface of NPs. Finally, the challenges and future perspectives of the field have been discussed. This review can be harnessed to exploit PSf for the development of safe and bio-inspired pulmonary drug delivery strategies.</p
Investigating the effect of [C8Py][Cl] and [C18Py][Cl] ionic liquids on the water/oil interfacial tension by considering Taguchi method
Capillary and interfacial forces are of great influences of trapping hydrocarbon in porous media after primary and secondary recovery processes. The trapped crude oil in the reservoir can be mobilized and produced by reducing these forces. Thus, surfactant flooding, as a main enhanced oil recovery (EOR) method, is usually applied to reduce the interfacial tension (IFT) of crude oil–water system in porous medium and improves the oil recovery. This study focused on the effect of [C8Py][Cl] and [C18Py][Cl] ionic liquids (ILs), as a new family of surfactant, in combination with various salts including sodium chloride, potassium chloride, magnesium sulfate and potassium sulfate on IFT reduction. EOR injection solutions were prepared from mixing the ILs at different concentrations of 100, 250, 500 and 1000 ppm with the salts ranging from 500 to 80,000 ppm. Obtained results showed that the minimum IFT value from both ILs was achieved when the concentration of the ILs was about 1000 g/mL, and the concentrations of KCl, K2SO4, MgSO4 and NaCl were 1000, 2000, 500 and 80,000 ppm, respectively. The minimum IFTs were achieved when NaCl and ILs concentrations were the maximum and MgSO4 concentration was the minimum
Prevention of liver cancer by standardized extract of Melissa officinalis L. in a rat model of hepatocellular carcinoma: Its potential role as a chemopreventive agent
Introduction: Hepatocellular carcinoma (HCC) is a primary malignancy of the liver and the third most common cause of cancer-related death worldwide. Melissa officinalis L. (M. officinalis L.), known as lemon balm is a medicinal plant, which has a wide range of pharmacological properties. This study was aimed to assess the chemopreventive effect of aqueous extract of M. officinalis (AMO) against diethyl nitrosamine (DEN)-induced hepatocellular carcinoma (HCC) in rats.
Methods and Results: The model of hepatocellular carcinoma was induced by a single intraperitoneal injection of DEN (200 mg/kg) as an initiator and after two weeks was followed by daily oral administration of 2-acetylaminofluorene (30 mg/kg) as a promoter for two weeks. Lemon balm-treated rats were pretreated with AMO intragastrically at three different doses two weeks prior to DEN injection. At the end of the experiment, the marked reduction of serum biomarkers of liver damage and cancer, including alfa-fetoprotein (AFP), gamma glutamyl transpeptidase (GGT), alanine transaminase (ALT), and aspartate transaminase (AST) were observed in AMO complemented rats compared to DEN-treated animals. Furthermore, the extract exhibited in vivo antioxidant activity by elevating GSH concentration and preventing lipid peroxidation in the liver tissues of HCC rats. The relative weight of liver was also reduced in lemon balm-treated rats as a prognostic marker in HCC.
Conclusion: Our findings demonstrated that M. officinalis has a chemopreventive effect against HCC in rats and can be suggested as a potential agent for the prevention of primary liver cancer.
 
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