Secrecy in Untrusted Networks

We investigate the protection of migrating agents against the untrusted sites they traverse. The resulting calculus provides a formal framework to reason about protection policies and security protocols over distributed, mobile infrastructures, and aims to stand to ambients as the spi calculus stands to ?. We present a type system that separates trusted and untrusted data and code, while allowing safe interactions with untrusted sites. We prove that the type system enforces a privacy property, and show the expressiveness of the calculus via examples and an encoding of the spi calculus

Improving the performance of the iterative signature algorithm for the identification of relevant patterns

The iterative signature algorithm (ISA) has become very attractive to detect co-regulated genes from microarray data matrices and can be a useful tool for the identification of similar patterns in many other kinds of numerical data matrices. Nevertheless, its algorithmic strategy exhibits some limitations since it is based on statistical behavior of the average and considers averages weighted by scores not necessarily positive. Hence, we propose to take the median instead of the average and to use absolutes scores in ISA's structure. Furthermore, a generalized function is also introduced in the algorithm in order to improve its algorithmic strategy for detecting high value or low value biclusters. The effects of these simple modifications on the performance of the biclustering algorithm are evaluated through an experimental comparative study involving synthetic data sets and real data from the organism Saccharomyces cerevisiae. The experimental results show that the proposed variations of ISA outperform the original version in many situations. Absolute scores in ISA are shown to be essential for the correct interpretation of the biclusters found by the algorithm. The median instead of the average turns the biclustering algorithm more resilient to outliers in the data sets. Copyright © 2011 Wiley Periodicals, Inc

Ontogenija usnog aparata salmo faroides and salmo macedonicus gajenih u mrestilištu tokom ranih faza razvitka

Continuing losses of natural production from over harvesting, habitat degradation and disappearance of spawning habitat due to hydroelectric development, irrigation, logging and transportation are increasingly showing the importance of hatchery operations in many countries. Few years ago, the Republic of Macedonia started with captive breeding programs for salmons. This program involves capturing wild fish of species as Salmo faroides and Salmo macedonicus from their native habitats and subsequent culturing the offspring from captive broodstocks which are then stocked into ancestral streams at the juvenile stage. From a practical point of view, the importance of study on how a developing larva copes with the changing functional demands during ontogeny, especially when being reared under artificial conditions, is obvious. Understanding how the locomotor and feeding apparatus is formed during early ontogeny can assist in improving the success of artificial propagation in terms of effective production of high quality juveniles. This would especially be valuable when offspring would be re-introduced into the river ecosystem. On the other hand knowledge on the ontogeny of fishes, especially for the early development of the skeletal system, provides information that can also be useful for solving some taxonomic problems and unravel phylogenetic relationships. For example, it is well known that morphological variation is commonly observed in salmonids. These fishes often form reproductively isolated populations across a diversity of environments and exhibit high levels of phenotypic variation. The final form of a phenotype and its life history are determined during early ontogeny. To better understand the relationship between morphology and ecology studies on the effect on environmentally induced variation in early life stage development within a single species, or study differences in the effect of a single environment in closely related species. Among the Salmo species that are present in the Balkan Peninsula, there is a high level of phenotypic variability, where also phenotypic plasticity is problematic for demarcate species boundaries between previously defined salmon species. Molecular data have confirmed the existence of previously defined species but several nominal species and populations of Balkan trout still remain unresolved. Still, understanding patterns of phenotypic variation that underlies molecular affinities remains essential. Within this context, we analysed the ontogeny of the skeletal system in Salmo faroides and Salmo macedonicus, two species of a still uncertain taxonomic status, reared under controlled condition. We wanted to test to what degree ontogeny of these closely related species is similar. In this study we focus on the early development of the feeding apparatus, from hatching till beginning of the exogenous feedin

Functional interaction between Drosophila olfactory sensory neurons and their support cells

Insects detect volatile chemicals using antennae, which house a vast variety of olfactory sensory neurons (OSNs) that innervate hair-like structures called sensilla where odor detection takes place. In addition to OSNs, the antenna also hosts various support cell types. These include the triad of trichogen, tormogen, and thecogen support cells that lie adjacent to their respective OSNs. The arrangement of OSN supporting cells occurs stereotypically for all sensilla and is widely conserved in evolution. While insect chemosensory neurons have received considerable attention, little is known about the functional significance of the cells that support them. For instance, it remains unknown whether support cells play an active role in odor detection, or only passively contribute to homeostasis, e.g., by maintaining sensillum lymph composition. To investigate the functional interaction between OSNs and support cells, we used optical and electrophysiological approaches in Drosophila. First, we characterized the distribution of various supporting cells using genetic markers. By means of an ex vivo antennal preparation and genetically-encoded Ca(2+) and K(+) indicators, we then studied the activation of these auxiliary cells during odor presentation in adult flies. We observed acute responses and distinct differences in Ca(2+) and K(+) fluxes between support cell types. Finally, we observed alterations in OSN responses upon thecogen cell ablation in mature adults. Upon inducible ablation of thecogen cells, we notice a gain in mechanical responsiveness to mechanical stimulations during single-sensillum recording, but a lack of change to the neuronal resting activity. Taken together, these results demonstrate that support cells play a more active and responsive role during odor processing than previously thought. Our observations thus reveal that support cells functionally interact with OSNs and may be important for the extraordinary ability of insect olfactory systems to dynamically and sensitively discriminate between odors in the turbulent sensory landscape of insect flight

PREGLED REZULTATA KISELOSTI SIROVOG MLEKA NA TERITORIJI OPŠTINE SJENICA

The aim of the work is to determine the acidity (pH) of milk produced on family farms in the Municipality of Sjenica depending on the season and month of production. It found an analysis of 3,226 milk samples that manufacturers and processors brought voluntarily to the lab. The process of receiving and analyzing the samples was done according to the Rulebook on the Quality of Raw Milk and ISO/IEC 17025:2017. The number of samples of raw milk in winter (529) is lower than in summer (1094). The average pH is the highest in the month of December (6.70), and the lowest in March (6.52). On the territory of the Municipality of Sjenica there was a steady acidity of milk per month in 2019. and moved within the boundaries envisioned in the Regulation on the Quality of Raw Milk.Publishe

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks

Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways

Positive correlation can be diversely instantiated as shifting, scaling or geometric pattern, and it has been extensively explored for time-course gene expression data and pathway analysis. Recently, biological studies emerge a trend focusing on the notion of negative correlations such as opposite expression patterns, complementary patterns and self-negative regulation of transcription factors (TFs). These biological ideas and primitive observations motivate us to formulate and investigate the problem of maximizing negative correlations. The objective is to discover all maximal negative correlations of statistical and biological significance from time-course gene expression data for enhancing our understanding of molecular pathways. Given a gene expression matrix, a maximal negative correlation is defined as an activation–inhibition two-way expression pattern (AIE pattern). We propose a parameter-free algorithm to enumerate the complete set of AIE patterns from a data set. This algorithm can identify significant negative correlations that cannot be identified by the traditional clustering/biclustering methods. To demonstrate the biological usefulness of AIE patterns in the analysis of molecular pathways, we conducted deep case studies for AIE patterns identified from Yeast cell cycle data sets. In particular, in the analysis of the Lysine biosynthesis pathway, new regulation modules and pathway components were inferred according to a significant negative correlation which is likely caused by a co-regulation of the TFs at the higher layer of the biological network. We conjecture that maximal negative correlations between genes are actually a common characteristic in molecular pathways, which can provide insights into the cell stress response study, drug response evaluation, etc

QUBIC: a qualitative biclustering algorithm for analyses of gene expression data

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called ‘scaling patterns’, a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/∼maqin/bicluster. A server version of QUBIC is also available upon request

Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms

<p>Abstract</p> <p>Background</p> <p>Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking.</p> <p>Results</p> <p>In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking.</p> <p>Conclusions</p> <p>Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.</p