Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel folate-modified self-microemulsifying drug delivery system of curcumin for colon targeting

Background The objective of this study was to prepare, characterize, and evaluate a folate-modified self-microemulsifying drug delivery system (FSMEDDS) with the aim to improve the solubility of curcumin and its delivery to the colon, facilitating endocytosis of FSMEDDS mediated by folate receptors on colon cancer cells. Methods Ternary phase diagrams were constructed in order to obtain the most efficient self-emulsification region, and the formulation of curcumin-loaded SMEDDS was optimized by a simplex lattice experiment design. Then, three lipophilic folate derivatives (folate-polyethylene glycol-distearoylphosphatidylethanolamine, folate-polyethylene glycol-cholesteryl hemisuccinate, and folate-polyethylene glycol-cholesterol) used as a surfactant were added to curcumin-loaded SMEDDS formulations. An in situ colon perfusion method in rats was used to optimize the formulation of FSMEDDS. Curcumin-loaded FSMEDDS was then filled into colon-targeted capsules and the in vitro release was investigated. Cytotoxicity studies and cellular uptake studies was used in this research. Results The optimal formulation of FSMEDDS obtained with the established in situ colon perfusion method in rats was comprised of 57.5% Cremophor® EL, 32.5% Transcutol® HP, 10% Capryol™ 90, and a small amount of folate-polyethylene glycol-cholesteryl hemisuccinate (the weight ratio of folate materials to Cremophor EL was 1:100). The in vitro release results indicated that the obtained formulation of curcumin could reach the colon efficiently and release the drug immediately. Cellular uptake studies analyzed with fluorescence microscopy and flow cytometry indicated that the FSMEDDS formulation could efficiently bind with the folate receptors on the surface of positive folate receptors cell lines. In addition, FSMEDDS showed greater cytotoxicity than SMEDDS in the above two cells. Conclusion FSMEDDS-filled colon-targeted capsules are a potential carrier for colon delivery of curcumin.
Systematic Analysis of Protein Pools, Isoforms, and Modifications Affecting Turnover and Subcellular Localization

In higher eukaryotes many genes encode protein isoforms whose properties and biological roles are often poorly characterized. Here we describe systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties. Using information from ion intensities we have estimated protein abundance levels and using rates of change in stable isotope labeling with amino acids in cell culture isotope ratios we measured turnover rates and subcellular distribution for the HeLa cell proteome. Protein isoforms were detected using three data analysis strategies that evaluate differences between stable isotope labeling with amino acids in cell culture isotope ratios for specific groups of peptides within the total set of peptides assigned to a protein. The candidate approach compares stable isotope labeling with amino acids in cell culture isotope ratios for predicted isoform-specific peptides, with ratio values for peptides shared by all the isoforms. The rule of thirds approach compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of the protein, assessing differences between segment values. The three in a row approach compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein. Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices. The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides. Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.
On the Tails of the Distribution of the Sum of Lognormals

Finding the distribution of the sum of lognormal random variables is an important mathematical problem in wireless communications, as well as in many other fields. While several methods exist to approximate this distribution, their performance tends to deteriorate in both tails. Finding a good overall fit remains an open problem. Other disadvantages of these methods are their complexity and, in some cases, their limitation to particular scenarios. In this paper we examine the sum of independent lognormal random variables with arbitrary parameters. We define the concept of best lognormal fit to a tail and show what it means in terms of convergence. We restate a known result about asymptotes to the higher tail of the distribution. To our knowledge, the lower tail has not yet been studied. We give a simple closed-form expression for an asymptote to the lower tail. We also show that known methods for finding the sum of lognormals use distribution functions that do not have this asymptotic behaviour in the tails. Our results are complementary to the existing knowledge, which together can combine to solve the problem of the sum of lognormals simply and exactly. We support our results by simulations.
How reliable is the pattern adaptation technique? A modeling study.

Upon prolonged viewing of a sinusoidal grating, the visual system is selectively desensitized to the spatial frequency of the grating, while the sensitivity to other spatial frequencies remains largely unaffected. This technique, known as pattern adaptation, has been so central to the psychophysical study of the mechanisms of spatial vision that it is sometimes referred to as the "psychologist's microelectrode. " While this approach implicitly assumes that the adaptation behavior of the system is diagnostic of the corresponding underlying neural mechanisms, this assumption has never been explicitly tested. We tested this assumption using adaptation bandwidth, or the range of spatial frequencies affected by adaptation, as a representative measure of adaptation. We constructed an intentionally simple neuronal ensemble model of spatial frequency processing and examined the extent to which the adaptation bandwidth at the system level reflected the bandwidth at the neuronal level. We find that the adaptation bandwidth could vary widely even when all spatial frequency tuning parameters were held constant. Conversely, different spatial frequency tuning parameters were able to elicit similar adaptation bandwidths from the neuronal ensemble. Thus, the tuning properties of the underlying units did not reliably reflect the adaptation bandwidth at the system level, and vice versa. Furthermore, depending on the noisiness of adaptation at the neural level, the same neuronal ensemble was able to produce selective or nonselective adaptation at the system level, indicating that a lack of selective adaptation at the system level cannot be taken to mean a lack of tuned mechanisms at the neural level. Together, our results indicate that pattern adaptation cannot be used to reliably estimate the tuning properties of the underlying units, and imply, more generally, that pattern adaptation is not a reliable tool for studying the neural mechanisms of pattern analysis.
An Improved Genetic Algorithm for Multiple Sequence Alignment Project Report of CSE 848 , Fall 2010

In this project we applied Genetic Algorithm (GA) to solve the problem of Multiple Sequence Alignment (MSA). MSA is one of the most useful tools in bioinformatics. It is widely used to identify conservation of protein domains, RNA secondary structure and classification of biological sequences. However, it is recognized as one of the most challenging tasks in bioinformatics. Most formulations of MSA lead to an an NP-hard problem. Thus most existing methods use heuristics approaches due to the exponential time complexity of global optimization. Genetic Algorithms (GAs) have been proposed to solve this problem and achieved some good performance. However, they suffer from problems such as premature convergence. We implemented conventional GA on this problem using ECJ, a research Evolutionary Computation system written in Java. Also we have made use of the idea of reserved area in our GA and compared its performance with conventional GA. We found that GA with reserved area (GARS) [1] successfully prevented premature and brought in improvement in MSA for short sequences. However, for the dataset with long sequences, there is no significant improvement.
An Optimal Dividends Problem with a Terminal Value for Spectrally Negative Lévy Processes with a Completely Monotone Jump Density

We consider a modified version of the classical optimal dividends problem of de Finetti in which the objective function is altered by adding in an extra term which takes account of the ruin time of the risk process, the latter being modeled by a spectrally negative Lévy process. We show that, with the exception of a small class, a barrier strategy forms an optimal strategy under the condition that the Lévy measure has a completely monotone density. As a prerequisite for the proof, we show that, under the aforementioned condition on the Lévy measure, the q-scale function of the spectrally negative Lévy process has a derivative which is strictly log-convex.
Array Size Reduction for High-Rank LOS MIMO ULAs

In this letter we propose an extended LOS MIMO channel model, which considers an additional phase shifting term in the transmission path, and which provides the potential to improve channel conditioning significantly. We show that this phase shifting can, for example, be achieved by adding a dielectric material between the transmitting and receiving antennas, where the phase shift is dependent on the distance the waves travel in the medium. Using that distance as a design parameter we demonstrate that the optimal spacing between antenna elements of uniform linear arrays, achieving full spatial multiplexing, can be reduced compared with the well-known spacing criterion from previous investigations.
Robust belief roadmap: Planning under uncertain and intermittent sensing

This paper considers the problem of planning a path for an autonomous vehicle from a start to a goal location in presence of sensor intermittency modeled as a stochastic process, in addition to process and measurement noise. The aim is to plan a path that minimizes the localizational uncertainty for the vehicle upon arriving at the goal location. The main contribution of this paper is two-fold. We first show that it is possible to obtain an analytical bound on the performance of a state estimator under sensor misdetection (intermittency) occurring stochastically over time. We then use this bound in a sample-based path planning algorithm to produce a path that trades off accuracy and robustness. This extends the recent body of work on planning under uncertainty to include the fact that sensors may not provide any measurement owing to misdetection. This is caused either by adverse environmental conditions that prevent the sensors from making measurements or by the fundamental limitations of the sensors. Examples include RF-based ranging devices that intermittently do not receive the signal from beacons because of obstacles or the misdetection of features by a camera system in detrimental lighting conditions. Computational results demonstrate the benefit of the approach and comparisons are made with the state of the art in path planning in belief space.
Feasibility grids for localization and mapping in crowded urban scenes

Localization and mapping are fundamental tasks in mobile robotics. State-of-the-arts often rely on the static world assumption using the occupancy grids. However, the real environment is typically dynamic. We propose the feasibility grids to facilitate the representation of both the static scene and the moving objects. The dual sensor models are introduced to discriminate between stationary and moving objects in mobile robot localization. Instead of estimating the occupancy states, the feasibility grids maintain the stochastic estimates of the feasibility (crossability) states of the environment. Given that an observation can be decomposed into stationary objects and moving objects, incorporating the feasibility grids in localization yields performance improvements over the occupancy grids, particularly in highly dynamic environments. Our approach is extensively evaluated using real data acquired with a planar laser range finder. The experimental results show that the feasibility grid is capable of rapid convergence and robust performance in mobile robot localization by taking into account moving object information. A root mean squares accuracy of within 50cm is achieved, without the aid of GPS, which is sufficient for autonomous navigation in crowded urban scenes. The empirical results suggest that the performance of localization can be improved when handling the changing environment explicitly.
A comparative analysis between the symmetric and the hybrid asymmetric nine-level series connected H-bridge cells inverter

This paper presents the comparison of two cascaded multilevel inverter, with phase-voltage of the nine levels and applied to drive an induction motor of 500 kVA/4160 V. Performance indices used in hereinafter in the comparison are semiconductors power losses, total harmonic distortion and first order distortion factor. The comparisons are developed in three ways; in the first comparison the switching frequency is found. It guarantees that the converters present the same efficiency. In the second comparison the objective is finding the maximum switching frequency that it is gotten in each topology. Finally, in the third comparison, the efficiency which both systems present the same output filter is found.
Optimal Social Security Policy in an Aging Economy with Supply Disturbance

Social security can serve as a tool to transfer wealth across generations and relieve the overaccumulation of capital. In this paper, I try to explore the capability of the social security system to deal with demographic and production shocks. Using a modied OLG model with uncertain life expectancy and production, I consider the optimum under dierent preferences of the government. I nd that if the government is
Biases in Static Oligopoly Models? Evidence from the California Electricity Market

Estimating market power is often complicated by a lack of reliable marginal cost data. A number of empirical studies identify industry competition and marginal cost levels by estimating the firms' first order condition within a conjectural variations framework. Few studies, however, have analyzed the accuracy of this technique. In this paper, we use direct measures of marginal cost for the California electricity market to measure the extent to which estimated mark-ups and marginal costs are biased. Our results suggest that the technique poorly estimates mark-ups and the sensitivity of marginal cost to cost shifters.
Approximating the permanent with fractional belief propagation

We discuss schemes for exact and approximate computations of permanents, and compare them with each other. Specifically, we analyze the Belief Propagation (BP) approach and its Fractional Belief Propagation (FBP) generalization for computing the permanent of a non-negative matrix. Known bounds and conjectures are verified in experiments, and some new theoretical relations, bounds and conjectures are proposed. The Fractional Free Energy (FFE) functional is parameterized by a scalar parameter $\gamma\in[-1;1]$, where $\gamma=-1$ corresponds to the BP limit and $\gamma=1$ corresponds to the exclusion principle (but ignoring perfect matching constraints) Mean-Field (MF) limit. FFE shows monotonicity and continuity with respect to $\gamma$. For every non-negative matrix, we define its special value $\gamma_*\in[-1;0]$ to be the $\gamma$ for which the minimum of the $\gamma$-parameterized FFE functional is equal to the permanent of the matrix, where the lower and upper bounds of the $\gamma$-interval corresponds to respective bounds for the permanent. Our experimental analysis suggests that the distribution of $\gamma_*$ varies for different ensembles but $\gamma_*$ always lies within the $[-1;-1/2]$ interval. Moreover, for all ensembles considered the behavior of $\gamma_*$ is highly distinctive, offering an emprirical practical guidance for estimating permanents of non-negative matrices via the FFE approach.
Neuronal Logic gates realization using Vedic mathematics

Gates are the fundamental building block of all logic circuits. Artificial neural networks (ANN) have processing capabilities in a parallel architecture, and due to this they are useful in applications like pattern recognition, system identification, prediction problems, robotics, and control problems. Boolean logic realization using artificial neural network is known as Neuronal Logic. Simple and low precision computations are the basic requirements of ANN which can be performed faster. This can be implemented on cheap and low precision hardware. Neural network involves enormous number of multiplication and addition calculations. It has been already proved that multipliers based on Vedic mathematics are faster in speed than the standard multipliers. In this paper, the possibility of hardware realization of neuronal logic gates using Vedic multipliers herein referred to as Vedic neuron has been explored. This is achieved by performing the neural network computations using Vedic mathematics rather than the conventional multiplication process. Basic logic gates like AND, OR and AND-NOT have been studied and its hardware implementation using neural network has been simulated using VHDL. A comparative study was carried out on the computation speed of neuronal logic gates implemented using conventional multipliers as well as neuronal logic gates implemented using Vedic multipliers. The increase in processing speed with Vedic neuron implementation has been observed which can be of use in several real time operations where speed is critical.
Quality Mesh Generation in Higher Dimensions

We consider the problem of triangulating a d-dimensional region. Our mesh generation algorithm, called QMG, is a quadtree-based algorithm that can triangulate any polyhedral region including nonconvex regions with holes. Furthermore, our algorithm guarantees a bounded aspect ratio triangulation provided that the input domain itself has no sharp angles. Finally, our algorithm is guaranteed never to overrefine the domain, in the sense that the number of simplices produced by QMG is bounded above by a factor times the number produced by any competing algorithm, where the factor depends on the aspect ratio bound satisfied by the competing algorithm. The QMG algorithm has been implemented in C++ and is used as a mesh generator for the finite element method.
Markerless human motion capture-based exercise feedback system to increase efficacy and safety of elder exercise routines

We developed a novel markerless motion capture system and explored its use in documenting elder exercise routines in a health club. We present two case studies of elders using a treadmill from our pilot work. Results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Our participants indicated that feedback from this system would add value to their exercise routines.
The role of assumptions in causal discovery

The paper looks at the conditional independence search approach to causal discovery, proposed by Spirtes et al. and Pearl and Verma, from the point of view of the mechanism-based view of causality in econometrics, explicated by Simon. As demonstrated by Simon, the problem of determining the causal structure from data is severely underconstrained and the perceived causal structure depends on the a priori assumptions that one is willing to make. I discuss the assumptions made in the independence search-based causal discovery and their identifying strength.
Instability of charged black holes in anti-de Sitter space

We exhibit a tachyonic mode in a linearized analysis of perturbations of large anti-de Sitter Reissner-Nordstrom black holes in four dimensions. In the large black hole limit, and up to a 0.7% discrepancy which is probably round-off error in the numerical analysis, the tachyon appears precisely when the black hole becomes thermodynamically unstable.
Word Length and Orthographic Neighborhood Size Effects in the Left and Right Cerebral Hemispheres

Previous studies have reported an interaction between visual field (VF) and word length such that word recognition is affected more by length in the left VF (LVF) than in the right VF (RVF). A reanalysis showed that the previously reported effects of length were confounded with orthographic neighborhood size (N). In three experiments we manipulated length and N in lateralized lexical decision tasks. Results showed that length and VF interacted even with N controlled (Experiment 1); that N affected responses to words in the LVF but not the RVF (Experiment 2); and that when length and N were combined, length only affected performance in the LVF for words with few neighbors.
A Clustering Algorithm Based on Square Inaccuracy Density

This paper proposed a kind of clustering algorithm based on square inaccuracy density. This algorithm adopted Square Inaccuracy density Function to fix on the parameter in clustering algorithm in order to avoid the problem caused by the random and subjective setting of the initial center of mass. In addition, this algorithm also brought in backtrack strategy to avoid the questions caught by the local solution. The experiment suggested that this algorithm can get a better result when it meets that the clustering points congregate in the form of sphere.
A Personalized Geographic-Based Diffusion Model for Location Recommendations in LBSN

Location Based Social Networks (LBSN) have emerged with the purpose of allowing users to share their visited locations with their friends. Foursquare, for instance, is a popular LBSN where users endorse and share tips about visited locations. In order to improve the experience of LBSN users, simple recommender services, typically based on geographical proximity, are usually provided. The state-of-the-art location recommenders in LBSN are based on linear combinations of collaborative filtering, geo and social-aware recommenders, which implies fine tuning and running three (or more) separate algorithms for each recommendation request. In this paper, we present a new location recommender that integrates collaborative filtering and geographic information into one single diffusion-based recommendation model. The idea is to learn a personalized ranking of locations for a target user considering the locations visited by similar users, the distances between visited and non visited locations and the regions he prefers to visit. We conduct experiments on real data from two different LBSN, namely, Go Walla and Foursquare, and show that our approach outperforms the state-of-art in most of the cities evaluated.
Attractor Dynamics in the Hippocampal Representation of the Local Environment

Memories are thought to be attractor states of neuronal representations, with the hippocampus a likely substrate for context-dependent episodic memories. However, such states have not been directly observed. For example, the hippocampal place cell representation of location was previously found to respond continuously to changes in environmental shape alone. We report that exposure to novel square and circular environments made of different materials creates attractor representations for both shapes: Place cells abruptly and simultaneously switch between representations as environmental shape changes incrementally. This enables study of attractor dynamics in a cognitive representation and may correspond to the formation of distinct contexts in context-dependent memory.