Miracle-dz/UniFAR-FTUs huggingface dataset:
Life Tables of Habrobracon hebetor (Hymenoptera: Braconidae) Parasitizing Anagasta kuehniella and Plodia interpunctella (Lepidoptera: Pyralidae): Effect of Host Density

Abstract The reproductive performance of the parasitoid Habrobracon hebetor (Say) (Hymenoptera: Braconidae) against the moths Anagasta kuehniella Zeller and Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) was studied in the laboratory. The analysis was based on the comparison of parasitoid’s life table parameters related to those of its hosts at various conditions of host density (daily supply of 1, 5, 15, and 30 full-grown host larvae). The estimated parameters were the intrinsic rate of natural increase (rm), the net reproductive rate (Ro), the mean generation time (G), the finite capacity of increase (λ), the gross reproductive rate (GRR), the doubling time (DT), the reproductive value (Vx), and the life expectancy (ex). The rm of H. hebetor proved to be significantly higher than those of its hosts at all host densities. When only one host per day was supplied, the wasp had the lowest reproductive potential, whereas it was maximized when 15 hosts per day were exposed. Maximum values of Ro and GRR were obtained at densities ≥15 host larvae per day. Any increase in host supply above this threshold did not cause significant changes in life table parameters. Variation of rm as a function of host density can be described by the linear regression. Sex ratio of wasp progeny (females/total) ranged from 0.36 to 0.42, irrespective of host density or species. Newly emerged adults recorded maximum ex and Vx. The results of this study can be used to improve mass rearing programs and inoculative release applications of H. hebetor against moth pests of stored products.
A New Novel Cluster based Analysis of Bank's Customer Data with Self-Organized Map Networks

the current global competing environment, creation of knowledge base and the use of it have been advantageous for the banks and the financial institutions and accounting and are being transformed to a strategic tool for competing among them and so data mining has been understood more and more in this field lately. In the today competing globe, banks and the financial institutions are trying to reach the advantage and be better than the others. Also, except execution of the business processes, the creation of the data knowledge and the use of it advantageous for the bank is being transformed to a strategic tool for competing. Taking into consideration this necessity, we have applied the Self-Organized Map (SOM) network in some cases of citizens in the banks of West Azerbaijan Province located at Republic Islamic of Iran. It is essential to cluster based solidarity analysis among of the specifications of customers to find common behavior points of them. However, it could be used to maintain the customers and find the new ones by the high responsible of programming of the banks. This approach leads to higher benefits and efficiencies in extracting and mining the likes and the wants of the customers. The results of the clustering analysis showed that the perspective of the customer about bank services and the effect of the electronic banking in banks selection, hold very similar junction patterns. KeywordsArtificial Neural Network, Self-Organized Map, Learning, Algorithm.
Analysis and Design of Millimeter-Wave Low-Voltage CMOS Cascode LNA With Magnetic Coupled Technique

In this paper, the design and analysis of CMOS low-noise amplifiers (LNAs) with a magnetic coupled technique in different cascode topologies are proposed. To minimize the noise figure (NF) and the supply voltage, and to guarantee large 3-dB bandwidth, transformers are designed and placed between the transistors of the cascode devices. Three low supply voltage and wideband LNAs are designed, fabricated, and tested for demonstration. The first LNA uses the magnetic coupled cascode configuration. The second LNA is consisted of magnetic coupled cascode configuration with two amplification stages. The third one-stage LNA is based on the magnetic coupled triple cascode configuration. The first LNA is designed at Q -band while the others are designed at V-band, using 90-nm low-power CMOS technology. The Q-band LNA has a gain of 13.8 dB and an NF of 3.8 dB at 37 GHz, with a power consumption of 18 mW at 1.2-V supply voltage. The V-band cascode LNA has a gain of 17 dB at 57 GHz and an NF of 4.4 dB at 59.5 GHz with a power consumption of 19.2 mW at 1.2-V supply voltage. The V -band triple-cascode LNA has a gain of 13.7 dB at 54 GHz and an NF of 5.3 dB at 59.5 GHz, with a power consumption of 14.4 mW at 1.2-V supply voltage. Compared with the conventional cascode LNAs, the proposed magnetic coupled cascode LNAs have better NF, larger gain bandwidth product, and lower power consumption. The use of the magnetic coupled technique in a multicascode LNA significantly improves the gain performance with a slight degradation of the NF.
Spatiotemporal alterations of cortical network activity by selective loss of NOS-expressing interneurons

Deciphering the role of GABAergic neurons in large neuronal networks such as the neocortex forms a particularly complex task as they comprise a highly diverse population. The neuronal isoform of the enzyme nitric oxide synthase (nNOS) is expressed in the neocortex by specific subsets of GABAergic neurons. These neurons can be identified in live brain slices by the nitric oxide (NO) fluorescent indicator diaminofluorescein-2 diacetate (DAF-2DA). However, this indicator was found to be highly toxic to the stained neurons. We used this feature to induce acute phototoxic damage to NO-producing neurons in cortical slices, and measured subsequent alterations in parameters of cellular and network activity. Neocortical slices were briefly incubated in DAF-2DA and then illuminated through the 4× objective. Histochemistry for NADPH-diaphorase (NADPH-d), a marker for nNOS activity, revealed elimination of staining in the illuminated areas following treatment. Whole cell recordings from several neuronal types before, during, and after illumination confirmed the selective damage to non-fast-spiking (FS) interneurons. Treated slices displayed mild disinhibition. The reversal potential of compound synaptic events on pyramidal neurons became more positive, and their decay time constant was elongated, substantiating the removal of an inhibitory conductance. The horizontal decay of local field potentials (LFPs) was significantly reduced at distances of 300–400 μm from the stimulation, but not when inhibition was non-selectively weakened with the GABAA blocker picrotoxin. Finally, whereas the depression of LFPs along short trains of 40 Hz stimuli was linearly reduced with distance or initial amplitude in control slices, this ordered relationship was disrupted in DAF-treated slices. These results reveal that NO-producing interneurons in the neocortex convey lateral inhibition to neighboring columns, and shape the spatiotemporal dynamics of the network's activity.
JAK2 tyrosine kinase mediates integrin activation induced by CXCL12 in B-cell chronic lymphocytic leukemia

Chemokines participate to B-cell chronic lymphocytic leukemia (B-CLL) pathogenesis by promoting cell adhesion and survival in bone marrow stromal niches and mediating cell dissemination to secondary lymphoid organs. In this study we investigated the role of JAK protein tyrosine kinases (PTK) in adhesion triggering by the CXC chemokine CXCL12 in normal versus CLL B-lymphocytes. We demonstrate that CXCL12 activates JAK2 in normal as well as CLL B-lymphocytes, with kinetics consistent with rapid adhesion triggering. By using complementary methodologies of signal transduction interference, we found that JAK2 mediates CXCL12-triggered activation of lymphocyte function-associated antigen-1 (LFA-1) and very late antigen-4 (VLA-4) integrins. We also show that JAK2 mediates the activation of the small GTP-binding protein RhoA, in turn controlling LFA-1 affinity triggering by CXCL12. Importantly, comparative analysis of 41 B-CLL patients did not evidence JAK2 functional variability between subjects, thus suggesting that JAK2, differently from other signaling events involved in adhesion regulation in B-CLL, is a signaling molecule downstream to CXCR4 characterized by a conserved regulatory role. Our results reveal JAK2 as critical component of chemokine signaling in CLL B-lymphocytes and indicate JAK inhibition as a potentially useful new pharmacological approach to B-CLL treatment.
Multitarget tracking with IP reversible jump MCMC-PF

In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. Bayesian multitarget tracking, in the Random Finite Set framework, propagates the multitarget posterior density recursively in time. Sequential Monte Carlo (SMC) approximations of the optimal filter are computationally expensive and lead to high-variance estimates as the number of targets increases. We present an extension of the Interacting Population-based MCMC-PF (IP-MCMC-PF) [1]. This extension exploits reversible jumps. Incorporation of Reversible Jump MCMC (RJMCMC) [2] methods into a tracking framework gives the possibility to deal with multiple appearing and disappearing targets, and makes the statistical inference more tractable. In our case, the technique is adopted to efficiently solve the high-dimensional state estimation problem, where the estimation of the existence and positions of many targets from a sequence of noisy measurements is required. Simulation analyses demonstrate that the proposed IP-RJMCMC-PF yields higher consistency, accuracy and reliability in multitarget tracking.
A Novel Family of Adaptive Filtering Algorithms Based on the Logarithmic Cost

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic cost inspired by the “competitive methods” from the online learning literature. The competitive or regret based approaches stabilize or improve the convergence performance of adaptive algorithms through relative cost functions. The new family elegantly and gradually adjusts the conventional cost functions in its optimization based on the error amount. We introduce important members of this family of algorithms such as the least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD) algorithms. However, our approach and analysis are generic such that they cover other well-known cost functions as described in the paper. The LMLS algorithm achieves comparable convergence performance with the least mean fourth (LMF) algorithm and enhances the stability performance significantly. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interferences and outperforms the sign algorithm (SA). We analyze the transient, steady-state and tracking performance of the introduced algorithms and demonstrate the match of the theoretical analyses and simulation results. We show the enhanced stability performance of the LMLS algorithm and analyze the robustness of the LLAD algorithm against impulsive interferences. Finally, we demonstrate the performance of our algorithms in different scenarios through numerical examples.
Component Classification and Retrieval Using Data Mining Techniques

A significant hurdle confronts the software reuser attempting to select candidate components from a software repository discriminating between those components without resorting to inspection of the implementation(s). In this paper We discuss data mining techniques to this problem which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary because all the existing techniques provides the static classification of components but the data mining techniques provides the dynamic classification of components, these techniques also provides an optimal balance between precision and recall rate.
Transitive closure algorithms based on graph traversal

Several graph-based algorithms have been proposed in the literature to compute the transitive closure of a directed graph. We develop two new algorithms (Basic_TC and Gobal_DFTC) and compare the performance of their implementations in a disk-based environment with a well-known graph-based algorithm proposed by Schmitz. Our algorithms use depth-first search to traverse a graph and a technique called marking to avoid processing some of the arcs in the graph. They compute the closure by processing nodes in reverse topological order, building descendent sets by adding the descendent sets of children. While the details of these algorithms differ considerably, one important difference among them is the time at which descendent set additions are performed. Basic_TC, results in superior performance. The first reason is that early additions result in larger descendent set sizes on the average over the duration of the execution, thereby causing more I/O; very often this turns out to more than offset the gains of not having to fetch certain sets again to add them. The second reason is that information collected in the first pass can be used to apply several optimizations in the second pass. To the extent possible, we also adapt these algorithms to perform path computations. Again, our performance comparison confirms the trends seen in reachability queries. Taken in conjunction with another performance study our results indicate that all graph-based algorithms significantly outperform other types of algorithms such as Seminaive and Warren.
Back Propagation Artificial Neural NetworkModels for Suspended Sediment Simulation âˆ’Case Studies From Four Indian Rivers

The magnitude of sediments transported by Rivers is a major concern for the water resources planning. The methods available for sediment estimation are largely empirical, with sediment rating curves being the most widely used, especially in India. In this paper, summary and comparison of four case studies carried out by the author where back propagation artificial neural network (ANN) models have been developed to simulate the suspended sediment load in four Indian rivers namely, Sutlej River of the Indus River system, Kosi River of the Ganges River system, Subansiri River of the Brahmaputra River system and Pranhita River of the Godavari River system. Daily data of sediment load and discharge have been used. A comparison has been made between the results obtained using ANNs and sediment rating curves. The suspended sediment load estimations in all the rivers obtained by ANNs have been found to be significantly superior to the corresponding classical sediment rating curve ones.
Risk of recurrence during follow-up for optimally treated advanced epithelial ovarian cancer (EOC) with a low-level increase of serum CA-125 levels.

BACKGROUND Our group evaluated the risk of recurrence for optimally treated advanced epithelial ovarian cancer (adEOC) in patients with a low-level rising serum CA-125 concentration within the normal range (0-35 kU/l). In addition, we tested the new proposed early CA-125 signal of progressive disease (EPD) criterion in the same study population. PATIENTS AND METHODS Patients treated from 1998 to 2006 for adEOC were identified at our institution. Inclusion criteria were as follows: CA-125 at time of diagnosis (>35 kU/l); International Federation of Gynecology and Obstetrics stages III-IV treated with optimal primary treatment; and complete response (CR) to primary treatment with normalization of CA-125. RESULTS Median progression-free survival and overall survival for the recurrence group (n = 60) were 17.7 and 38.2 months, respectively. The median follow-up time from CR to last contact was 40.2 months for patients in the nonrecurrence group (n = 36). An absolute increase in serum CA-125 levels of >or=5 kU/l compared with baseline CA-125 nadir values was significantly predictive of recurrence (odds ratio for recurrence = 402.98, P < 0.0001). The progression date was predated by the EPD criterion in 77% of patients with known progressive disease (median, 58 days early) with a sensitivity of 90%, a positive predictive value of 96.4%, and a false-positive rate of 5.6%. CONCLUSIONS Among patients with optimally treated adEOC in complete remission, a low-level increase in serum CA-125 concentration within the normal range is a strong independent predictive factor for disease recurrence. In this patient population, future prospective randomized trials should consider the evaluation of the EPD criterion.
Financialization and the third sector: Innovation in social housing bond markets

The recent global financial crisis has seen investors turn away from real-estate bonds, given their role in distributing risk during the crisis. However, since 2009, a new type of real-estate bond market has grown in London, enabling social housing groups to issue bonds. This could be viewed as further evidence of the extension of financialization practices into new spaces, beyond those of traditional capital markets and associated intermediaries. In this paper, we examine how financialization has begun to permeate the third sector, reordering the priority of housing associations' values, displacing social value creation with the economic. We highlight how reduced state funding has led social housing providers to become more reliant on capital market intermediaries, and explore how locally orientated social housing associations have become embedded within wider financial networks. While policy makers have viewed financial markets as a panacea to fund social housing developments in an age of austerity, tensions have emerged, requiring localized social housing organizations to become more commercial in their activities, jeopardizing their ability to protect vulnerable communities through social value creation.
Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters.
High-precision MRI-compatible force sensor with parallel plate structure

This paper presents an MRI-compatible optical force sensor: its principle, structure and performance. Our force sensor employs an optical micrometry based on differential measure of light intensity. This technology enables highly accurate and sensitive two degrees-of-freedom displacement sensing by using a point source, a photo detector and an optical lens. The developed force sensor achieves MRI compatibility by placing all the electric and metallic components outside MRI, and connecting the light path between the optical components by using multi-core optical fibers. The sensor head component has parallel plate structure and is made of glass fiber reinforced poly-ether-ether-keton those help reducing axial interference and hysteresis characteristics of plastic resin. The accuracy of this force sensor is better than 1.6 %F.S. under the applied force ranging from 0 to 3 N.
New Genes Involved in Osmotic Stress Tolerance in Saccharomyces cerevisiae

Adaptation to changes in osmolarity is fundamental for the survival of living cells, and has implications in food and industrial biotechnology. It has been extensively studied in the yeast Saccharomyces cerevisiae, where the Hog1 stress activated protein kinase was discovered about 20 years ago. Hog1 is the core of the intracellular signaling pathway that governs the adaptive response to osmotic stress in this species. The main endpoint of this program is synthesis and intracellular retention of glycerol, as a compatible osmolyte. Despite many details of the signaling pathways and yeast responses to osmotic challenges have already been described, genome-wide approaches are contributing to refine our knowledge of yeast adaptation to hypertonic media. In this work, we used a quantitative fitness analysis approach in order to deepen our understanding of the interplay between yeast cells and the osmotic environment. Genetic requirements for proper growth under osmotic stress showed both common and specific features when hypertonic conditions were induced by either glucose or sorbitol. Tolerance to high-glucose content requires mitochondrial function, while defective protein targeting to peroxisome, GID-complex function (involved in negative regulation of gluconeogenesis), or chromatin dynamics, result in poor survival to sorbitol-induced osmotic stress. On the other side, the competitive disadvantage of yeast strains defective in the endomembrane system is relieved by hypertonic conditions. This finding points to the Golgi-endosome system as one of the main cell components negatively affected by hyperosmolarity. Most of the biological processes highlighted in this analysis had not been previously related to osmotic stress but are probably relevant in an ecological and evolutionary context.
On Limitations of Universal Simulation : Constant-Round Public-Coin Zero-Knowledge Proofs Imply Understanding Programs

In this paper we consider the problem of whether there exist non-trivial constant-round public-coin zero-knowledge (ZK) proofs. We focus on the type of ZK proofs that admit a universal simulator (which handles all malicious verifiers), and show a connection between the existence of such proof systems and a seemingly unrelated “program understanding” problem: for a natural class of constant-round public-coin ZK proofs (which we call “canonical,” since all known ZK protocols fall into this category), a universal simulator can actually be used (as an oracle) to distinguish a non-trivial property of the verifier’s program. Our result can be viewed as new and extended evidence against the existence of constantround public-coin ZK proofs, since the above program-understanding problem, a typical goal in reverse-engineering attempts, is commonly believed to be notoriously hard—in general, and particularly so in the case of limited straight-line simulators. The earlier negative evidence for the above is Barack, Lindell and Vadhan [FOCS ’03]’s result, which was based on the incomparable assumption of the existence of certain entropy-preserving hash functions, now known not to be achievable from standard assumptions. Our reduction combines a careful analysis of the behavior of a set of verifiers in the above protocols and simulation, with a key tool which is an improved structure-preserving version of the well-known Babai-Moran Speedup (de-randomization) Theorem.