Miracle-dz/UniFAR-FTUs huggingface dataset:
Concentrative sorting of secretory cargo proteins into COPII-coated vesicles

Here, we show that efficient transport of membrane and secretory proteins from the ER of Saccharomyces cerevisiae requires concentrative and signal-mediated sorting. Three independent markers of bulk flow transport out of the ER indicate that in the absence of an ER export signal, molecules are inefficiently captured into coat protein complex II (COPII)-coated vesicles. A soluble secretory protein, glycosylated pro–α-factor (gpαf), was enriched ∼20 fold in these vesicles relative to bulk flow markers. In the absence of Erv29p, a membrane protein that facilitates gpαf transport (Belden and Barlowe, 2001), gpαf is packaged into COPII vesicles as inefficiently as soluble bulk flow markers. We also found that a plasma membrane protein, the general amino acid permease (Gap1p), is enriched approximately threefold in COPII vesicles relative to membrane phospholipids. Mutation of a diacidic sequence present in the COOH-terminal cytosolic domain of Gap1p eliminated concentrative sorting of this protein.
Estimation and Rejection of Unknown Sinusoidal Disturbances in Non-minimum-phase Nonlinear Output-Feedback Systems

This paper deals with adaptive estimation of unknown disturbances in a class of nonminimum phase nonlinear systems, and the stabilization and disturbance rejection based on the estimated disturbances. The unknown disturbances are combination of sinusoidal disturbances with unknown frequencies, unknown phases and amplitudes. The only information of the unknown disturbances is the number of distinctive frequencies inside. The class of nonlinear systems considered in this paper consists of nonlinear systems in the output feedback form and the systems may be nonminimum phase, ie, with unstable zero dynamics. An adaptive estimation algorithm is developed to give exponentially convergent estimates of the unknown disturbance and the system states. The asymptotic convergent estimates of unknown frequencies are also obtained. The proposed estimation algorithm works for both minimum phase and nonminimum phase nonlinear systems in output feedback form. Disturbance rejection with stabilisation is achieved by combining the control designed for the stabilisation of the disturbance-free system and the exponentially convergent estimate of the disturbance. Under the conditions specified for the control design of the system with no disturbance, the overall stability and complete rejection of the unknown disturbance are guaranteed by the proposed control algorithm.
Opinion Polarity Detection - Using Word Sense Disambiguation to Determine the Polarity of Opinions

In this paper, we present an unsupervised method for determining the polarity of opinions. It uses a word sense disambiguation algorithm to determine the correct sense of the words in the opinion. The method is also based on SentiWordNet and General Inquirer to determine the polarity of the senses. Due to the characteristics of these external resources, the proposed method does not depend on the knowledge domain and can be extended to other languages. In the evaluation carried out over the SemEval Task No. 14: Affective Text data our method outperforms both unsupervised and supervised systems presented in this task.
Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment

This paper studies the viability of concurrent object pose tracking and tactile sensing for assessing grasp stability on a physical robotic platform. We present a kernellogistic-regression model of poseand touch-conditional grasp success probability. Models are trained on grasp data which consist of (1) the pose of the gripper relative to the object, (2) a tactile description of the contacts between the object and the fully-closed gripper, and (3) a binary description of grasp feasibility, which indicates whether the grasp can be used to rigidly control the object. The data is collected by executing grasps demonstrated by a human on a robotic platform composed of an industrial arm, a three-finger gripper equipped with tactile sensing arrays, and a vision-based object pose tracking system. The robot is able to track the pose of an object while it is grasping it, and it can acquire grasp tactile imprints via pressure sensor arrays mounted on its gripper’s fingers. We consider models defined on several subspaces of our input data – using tactile perceptions or gripper poses only. Models are optimized and evaluated with f fold cross-validation. Our preliminary results show that stability assessments based on both tactile and pose data can provide better rates than assessments based on tactile data alone.
Brain Responses during the Anticipation of Dyspnea

Dyspnea is common in many cardiorespiratory diseases. Already the anticipation of this aversive symptom elicits fear in many patients resulting in unfavorable health behaviors such as activity avoidance and sedentary lifestyle. This study investigated brain mechanisms underlying these anticipatory processes. We induced dyspnea using resistive-load breathing in healthy subjects during functional magnetic resonance imaging. Blocks of severe and mild dyspnea alternated, each preceded by anticipation periods. Severe dyspnea activated a network of sensorimotor, cerebellar, and limbic areas. The left insular, parietal opercular, and cerebellar cortices showed increased activation already during dyspnea anticipation. Left insular and parietal opercular cortex showed increased connectivity with right insular and anterior cingulate cortex when severe dyspnea was anticipated, while the cerebellum showed increased connectivity with the amygdala. Notably, insular activation during dyspnea perception was positively correlated with midbrain activation during anticipation. Moreover, anticipatory fear was positively correlated with anticipatory activation in right insular and anterior cingulate cortex. The results demonstrate that dyspnea anticipation activates brain areas involved in dyspnea perception. The involvement of emotion-related areas such as insula, anterior cingulate cortex, and amygdala during dyspnea anticipation most likely reflects anticipatory fear and might underlie the development of unfavorable health behaviors in patients suffering from dyspnea.
Quick and effective method of bone marrow mesenchymal stem cell extraction

Abstract Background. Mesenchymal stem cells (MSCs) are currently exploited in numerous clinical trials to investigate their potential in immune regulation, hematopoesis or tissue regeneration. The most common source of MSCs for clinical use is human bone marrow. To generate sufficient numbers of cells relevant to clinical use in most cases the high volumes (20-50 ml) of bone marrow aspirates are taken. Methods. In this pilot study, 8 healthy bone marrow donors were included. Two different MSC extraction methods were evaluated: MSCs extraction from 60 ml of bone marrow using density gradient and MSCs extraction from 6 ml using red blood cell (RBC) lysis. Results. Our results showed that after RBC lysis the efficient amount of human MSCs can be isolated from 10 times less bone marrow volume (6 ml). Moreover, using small volume of bone marrow the adequate therapeutical dose of MSCs could be achieved during similar period of time (3-4 weeks). In conclusion, we have shown that MSCs isolation using RBC lysis is an effective and more advantageous method in comparison to standard MSCs isolation using density-gradient. Using RBC lysis from small volume of bone marrow the same amount of MSCs were obtained as usually using large volume and density-gradient.
Online state and parameter estimation of the Li-ion battery in a Bayesian framework

Due to an ever-growing role of lithium-ion batteries in industry, particularly automotive industry, an effective battery management system (BMS) is of critical importance. A reliable battery state estimation scheme is an integral part of such a BMS. Complicated nature of battery dynamics, weak observability, lack of knowledge about the degradation mechanisms of these batteries, etc has made their state estimation a challenging task. Among the published works on Li-ion battery estimation, a subject that has not received a great deal of attention is parameter estimation of the battery. Parameter estimation has a direct impact on both state of charge and state of health estimation of the battery. Most of the works in the field of battery estimation are built upon the known parameters of the battery whereas in reality these parameters change over-time and may not be known a priori, particularly for aged batteries. This work tackles the problem of parameter and state estimation of lithium-ion batteries from a model-based perspective using a multi-rate particle filter. This filter is applicable to the full-electrochemical of the battery without any restrictive assumption or simplification of the model equations. The filter is proposed in a multi-rate structure in order to address the run-time of the process and computational load of the algorithm. The simulation studies demonstrate the effectiveness of the proposed algorithm.
Enhancing the tractability of rely/guarantee specifications in the development of interfering operations

Various forms of assumption/commitment specifications have been used to specify and reason about the interference that comes from concurrent execution; in particular, consistent and complete proof rules relating to shared state operation specifications ‐with rely and guarantee conditions‐ have been published elsewhere. This paper discusses some issues about the formulation of such specifications and the way to record design decisions so as to make the use of rely/guarantee conditions more tractable.
Dividing Goods or Bads Under Additive Utilities

When utilities are additive, we uncovered in our previous paper (Bogomolnaia et al. "Dividing Goods or Bads under Additive Utilities") many similarities but also surprising differences in the behavior of the familiar Competitive rule (with equal incomes), when we divide (private) goods or bads. The rule picks in both cases the critical points of the product of utilities (or disutilities) on the efficiency frontier, but there is only one such point if we share goods, while there can be exponentially many in the case of bads. We extend this analysis to the fair division of mixed items: each item can be viewed by some participants as a good and by others as a bad, with corresponding positive or negative marginal utilities. We find that the division of mixed items boils down, normatively as well as computationally, to a variant of an all goods problem, or of an all bads problem: in particular the task of dividing the non disposable items must be either good news for everyone, or bad news for everyone. If at least one feasible utility profile is positive, the Competitive rule picks the unique maximum of the product of (positive) utilities. If no feasible utility profile is positive, this rule picks all critical points of the product of disutilities on the efficient frontier
Image Adaptive Watermarking Technique for Digital Phone

Digital watermarking has been proposed as a solution to the problem of copyright protection of digital images in digital phone camera. There are two important issues that watermarking algorithms need to address. First, watermarking schemes are required to provide trustworthy evidence for protecting rightful ownership. Second, good watermarking schemes should satisfy the requirement of robustness and resist distortions due to common image manipulations. This paper presents a blind image watermarking scheme for camera phone. This method is based on singular value decomposition (SVD) and wavelet decomposition. Analysis and experimental results show that the proposed watermarking method performs well in security and robustness of digital phone camera
Fast classification of handwritten on-line Arabic characters

Delaying the analysis launch until the completion of the handwritten word scribing, restricts on-line recognition systems to meet the highly responsiveness demands expected from such applications, and prevents implementing advanced features of input typing such as automatic word completion and real-time automatic spelling. This paper proposes an efficient Arabic handwritten characters recognizer aimed at facilitating real-time handwritten script analysis tasks. The fast classification is enabled by employing an efficient embedding of the feature vectors into a normed wavelet coefficients domain in which the Earth Movers Distance metric is approximated using the Manhattan distance. A sub-linear time character classification is achieved by utilizing metric indexing techniques. Using the results of the top ranked shapes of each predicted character, a list of candidate shapes of Arabic word parts is generated in a filter and refine approach to enable fast yet accurate recognition results in a dictionary-free environment. The system was trained and tested on characters and word parts extracted from the ADAB database, and promising accuracy and performance results were achieved.
DESIGN INFORMATION OPTIMUM VECTOR MACHINE LEARNING SYSTEMS

An explosively-assisted fuse is passively triggered (without electronics) by means of an auxiliary fuse in series with the main conductor. The auxiliary fuse provides a detonating voltage across a gap formed therein on melting in response to a fault current. The auxiliary fuse is configured to facilitate sizing for a variety of current loads with a standard fuse configuration.
Accelerating high performance applications with CUDA and MPI

Compute Unified Device Architecture (CUDA) programmed,Graphic Processing Units (GPUs) are rapidly becoming a major choice in high performance computing. Hence, the number of applications ported to the CUDA platform is growing high. Message Passing Interface(MPI) has been the choice of high performance computing for more than a decade and it has proven its capability in delivering higher performance in parallel applications. CUDA and MPI use different programming approaches but both of them depend on the inherent parallelism of the application to be effective. However, much less research had been carried out to evaluate the performance when CUDA is integrated with other parallel programming paradigms. This paper investigates on integration of these capabilities of both programming approaches and how we can achieve superior performance in general purpose applications. Thus, we have experimented CUDA+MPI programming approach with two well-known algorithms (Strassens Algorithm & Conjugate Gradient Algorithm) and shown how we can achieve higher performance by means of using MPI as computation distributing mechanism and CUDA as the main execution engine. We have developed a general purpose matrix multiplication algorithm and a Conjugate Gradient algorithm using CUDA and MPI. In this approach, MPI functions as the data distributing mechanism between the GPU nodes and CUDA as the main computing engine. This allows the programmer to connect GPU nodes via high speed Ethernet without special technologies. Thus, the programmer is enabled to view each GPU node separately as they are and to execute different components of a program in several GPU nodes.
A randomized steiner tree approach for biomarker discovery and classification of breast cancer metastasis

Motivation: DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of metastatic breast cancer a leading cause of cancer-related deaths in women worldwide. Recently, pathway-level relationships between genes have been increasingly used to build more robust classification models which also can provide useful biological insights. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a way to investigate the developmental process of breast cancer. Here, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers. Results: We applied this approach on three breast cancer microarray datasets, and identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are enriched with biological processes and pathways related to carcinogenic process, and, importantly, have higher stability across different datasets than in previous studies. Furthermore, those genes significantly increased cross-data classification accuracy in breast cancer metastasis. The randomized Steiner tree based approach described here is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. The analysis is limited here only to breast cancer, but can be easily applied
Clinical Diagnostic Implications of Body Fluid MiRNA in Oral Squamous Cell Carcinoma

Abstract Oral cancer, predominantly oral squamous cell carcinoma (OSCC), is one of the most leading causes of cancers worldwide. Due to a low 5-year survival rate, highly effective methods for the early detection of OSCC are totally needed. MicroRNAs (miRNAs), as promising biomarkers, can bring insights into tumorigenesis of oral cancers. However, studies on the accuracy of miRNAs detection in OSCC have inconsistent conclusions, leading us to conduct this meta-analysis. The aim of this study was to systematically review the articles investigating the diagnostic value of miRNAs in OSCC. The PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), Web of Science were searched (updated to June 11th, 2015) to identify all articles evaluating the diagnostic yield of miRNAs for OSCC. The pooled sensitivity, specificity, and other diagnostic parameters were used to assess the performance of miRNAs assays on OSCC detection. Statistical analysis was conducted by employing the R software. The present meta-analysis comprised 23 studies from 10 articles, including 598 OSCC patients and 320 healthy individuals, available for analysis. The summary receiver operator characteristic (SROC) curve was plotted. Meanwhile, the pooled diagnostic parameters and the area under curve (AUC) were calculated based on all included studies. The pooled diagnostic parameters calculated from all 23 studies were as follows: pooled sensitivity of 0.759 (95% CI: 0.701–0.809), pooled specificity of 0.773 (95% CI: 0.713–0.823) and AUC of 0.832, which indicates a relatively high diagnostic accuracy of miRNAs in differentiating OSCC patients from healthy controls. Meanwhile, In addition, subgroup analyses were conducted to access the heterogeneity between studies, which is based on specimen (serum/plasma/blood/saliva/ tissue) and ethnicity (Asian/Caucasian). In summary, our meta-analysis suggests that miRNAs might be used in noninvasive screening tests for OSCC, which needs further large-scale studies to be validated.
Relationships between Water, Otolith, and Scale Chemistries of Westslope Cutthroat Trout from the Coeur d'Alene River, Idaho: The Potential Application of Hard-Part Chemistry to Describe Movements in Freshwater

Abstract We quantified Mg:Ca, Mn:Ca, Sr:Ca, and Ba:Ca molar ratios from an area representing the summer 2000 growth season on otoliths and scales from 1-year-old westslope cutthroat trout Oncorhyncus clarki lewisi collected from three streams in the Coeur d'Alene River, Idaho, system. We also quantified Mg:Ca, Sr:Ca, and Ba:Ca molar ratios in the water during summer 2000 and used regressions to model the assimilation of the various ratios into the otoliths and scales. Otolith and scale chemistries were linearly related to the Sr:Ca and Ba:Ca ratios in the water. The partition coefficients for the Sr:Ca ratios in otoliths and scales are higher in this freshwater system than in experimental results from a saline environment; we attribute these differences to differences in the biology of saltwater and freshwater fish. In contrast, the Ba:Ca partition coefficients are similar between the two environments, suggesting that our estimates are representative of those for a wide range of concentrations, temperatur.
On the concert of Predictable Broadcast Reckon (PBR) for wireless mesh networks used in VANETS

The Predictable Broadcast Reckon (PBR) metric is an advanced routing metric for finding high-throughput paths in multi-hop wireless networks. However, it has been determined that PBR is not immune to load sensitivity and route oscillations in a single radio environment. Route oscillations refer to the situation where packet Broadcast switches between two or more routes due to congestion. This has the effect of degrading performance of the network, as the routing protocol may select a non optimal path. In this paper we avoid the route oscillation problem using a route stabilization technique which forces data Broadcast on a fixed route. We implement this solution in a popular routing protocol, AODV, by disabling both error messages and periodic updating messages. Therefore, packet Broadcasts will stay on the routes initially found by AODV. PBR is compared with a widely used routing metric, HOPS, for reference purposes. We find PBR greatly improves initial route selection in AODV compared to HOPS in networks in which only single flows exists. For networks in which there are multiple simultaneous flows, PBR behaves similar to HOPS in initial route selection. Although the known cause of performance degradation is eliminated, the PBR metric still shows anomalous behavior. We determine that a major cause of the poor performance of PBR is additional collisions due to extra overhead. We propose a modified solution in which we repeatedly broadcast RREQ (Route Request) packets. Simulation results show that our modified solution improves PBR in the initial route selection in both single flows and multiple flows cases.
Efficient parameter estimation for self-similar processes

On demontre la mortalite asymptotique de l'estimateur de maximum de vraisemblance pour les parametres d'un processus de Gauss dependant a long terme. Pour de tels processus on deduit aussi la limite de la matrice d'information de Fisher ce qui implique l'efficacite de l'estimation et d'une autre estimation de maximum de vraissemblance approximatif etudie pour d'autres auteurs. On deduit les resultats en se servant de proprietes asymptotiques de matrices de Toeplitz et d'une propriete d'equicontinuite de formes quadratiques
A model based approach for debugging embedded systems in real-time

Model driven design and development of real-time embedded systems has been gaining a lot of attention in the recent past. It is imperative for a hard real-time embedded system to execute and respond to sequenced exchanges of messages under critical temporal constraints. The UML provides artifacts such as communication diagrams, sequence diagrams and timing diagrams to aid software engineers to model and precisely document such a schedule of interactions or state changes at the design level. While modeling at the design level is interesting on one hand, it is also significant to understand the run time behavior of the embedded software. A step forward in this direction is to provide a real-time representation of the target system behavior on the host side. We propose an approach to realize a target debugger on the host side with a monitor solution in the target side. With the back annotated information from the target, a visual representation of the system behavior in real-time is now available at the design level. This way, a debugging solution in real-time is provided, thus taking a step forward towards understanding the behavior of the embedded software at hand. We illustrate our proposed approach with a prototype and examples.