Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone Marrow and Nonbone Marrow Toll Like Receptor 4 Regulate Acute Hepatic Injury Induced by Endotoxemia

Background Toll-like receptors (TLRs) are expressed in immune cells and hepatocytes. We examined whether hepatic Toll-like receptor 4 (TLR4) is involved in the acute hepatic injury caused by the administration of lipopolysaccharide (LPS) (septic shock model). Methods Wild type (WT), TLR4-deficient and chimera mice underwent myeloablative bone marrow transplantation to dissociate between TLR4 expression in the liver or in the immune-hematopoietic system. Mice were injected with LPS and sacrificed 4 hours later. Results Compared to TLR4 deficient mice, WT mice challenged with LPS displayed increased serum liver enzymes and hepatic cellular inflammatory infiltrate together with increased serum and hepatic levels of interleukin 1β (IL-1β), tumor necrosis factor α (TNFα) ,Up-regulation of hepatic mRNA encoding TLR4, IκB and c-jun expressions. TLR4 mutant mice transplanted with WT bone marrow were more protected than WT chimeric mice bearing TLR4 mutant hemopoietic cells from LPS, as seen by IL-1β and TNFα levels. We then used hepatocytes (Huh7) and macrophages from monocytic cell lines to detect TLR mRNA expression. Macrophages expressed a significantly higher level of TLR4 mRNA and TLR2 (more than 3000- and 8000-fold respectively) compared with the hepatocyte cell line. LPS administration induced TLR4 activation in a hepatocyte cell line in a dose dependent manner while TLR2 mRNA hardly changed. Conclusions These results suggest that TLR4 activation of hepatocytes participate in the immediate response to LPS induced hepatic injury. However, in this response, the contribution of TLR4 on bone marrow derived cells is more significant than those of the hepatocytes. The absence of the TLR4 gene plays a pivotal role in reducing hepatic LPS induced injury.
Dynamic Group Discovery and Configuration for Automated Swarms

With the advances in wireless ad hoc communications, robotics and microflyer technology, it will be possible to organize groups of small unmanned ground, sea and airborne vehicles and to launch complex missions that comprise several such groups. As the mission evolves, groups are reconfigured and individual mobile vehicles move from one group to another to meet dynamically changing requirements. In such a scenario, dynamic mission arrangement is carried out by the application layer, which may not communicate the new mission grouping information to the network layer. The network layer must thus ”discover” and configure the groups independently. In this paper, we introduce a dynamic group discovery and configuration scheme that aggregates nodes based on movement affinity. We also present a request-driven retrieval scheme, which provides services for discovering dynamically formed nodes’ group information. The combination of the two schemes supports Landmark Ad Hoc Routing (LANMAR) protocol in providing efficient and scalable routing operation for the complex, large-scale multi-level swarm missions. Keywords— Mobile ad hoc networks, swarm operation, group mobility, ad hoc routing.
Coreference resolution in biomedical full-text articles with domain dependent features

Coreference resolution is one of the most significant and difficult tasks in text mining. This paper proposes an algorithm to find coreference relations in biomedical papers. We concentrate on noun phrases referring to biomedical entities and do our research on biomedical full-text articles instead of paper abstract. The algorithm not only uses features about linguistic but also introduces features derived from biomedical domain knowledge to deeply reveal the coreference relation between biomedical noun phrases. These features are employed to a probabilistic-based classifier which takes the dependence of features into account. Experiments show that the features are helpful and the algorithm is effective for coreference resolution in the biomedical domain. On a full-text corpus with anaphoric links, a satisfactory experimental result (76.2% precision, 66.0% recall and 70.7% F-Measure) is achieved by the proposed algorithm. Due to appropriate features, there is 13.8% improvement yielded against the former algorithm.
Scaling up fast evolutionary programming with cooperative coevolution

Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, most analytical and experimental results on EP have been obtained using low-dimensional problems. It is interesting to know whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. It was discovered that neither classical EP (CEP) nor fast EP (FEP) performed satisfactorily for some large-scale problems. The paper shows empirically that FEP with cooperative coevolution (FEPCC) can speed up convergence rates on the large-scale problems whose dimension ranges from 100 to 1000. Cooperative coevolution adopts the divide-and-conquer strategy. It divides the system into many modules, and evolves each module separately and cooperatively. The results of FEPCC on the problems investigated here are something of a surprise. The time used by FEPCC to find a near optimal solution appears to scale linearly; that is, the time used seems to go up linearly as the dimensionality of the problems studied increases.
Geometry-Based Learning Algorithms

We present CHILS , the Convex Hull Inductive Learning System, a novel supervised learning algorithm based on approximating concepts with sets of convex hulls. We introduce a theoretical methodology for describing the power of a concept representation language and use it to compare convex hulls with other geometrical concept representations. The Domain Transform framework (DT) provides a clear way to compare the power of supervised learning systems, allowing us to characterize a class of domains which is learnable by some systems but cannot be learned by other systems. DT can be used similarly to compare the expected generalization performance of different domains.
A Survey of Computer Vision Research for Automotive Systems

Current autonomous driving technology heavily relies on a special sensor, Velodyne Sensor, and a detailed precision map. From Darpa Grand Challenge in 2005 to current Google Car, Velodyne Lidar sensors are used widely. They could provide 360 degree real-time 3D distance map by illuminating environment with lasers and analyzing the reflect light. The 3D distance map can be used in obstacle detection and navigation. However, according to the marketing report, Velodyne LIDAR scanner used in Google Car costs 70,000 dollars which is much higher than the average budget people expect. Another constrain Google Car facing is a high definition inch-precision map. For example, the map should include the height of traffic light. That means Google Car can only drive when the precision map is provided. It limits usage of Google Car to only small areas. Although these two technologies provide important functions for autonomous driving, they also make universalizing autonomous car become impossible.
Preliminary Results of Femtosecond Laser-assisted Cataract Surgery in a Private Clinic in Iran

Purpose: To report the preliminary results of femtosecond laser-assisted cataract surgery in Iranian patients. Methods: This prospective case series included 21 eyes of 21 patients with cataract. Mean patient age was 66.7 ± 10 years. The patients underwent femtosecond-laser assisted cataract surgery (VICTUS Femtosecond Laser Platform: Bausch + Lomb) and intraocular lens (IOL) implementation in Bina Eye Hospital, Tehran, Iran between May and October, 2014. Visual outcomes, intraocular pressure (IOP), and complications were evaluated three months after surgery. Results: Mean preoperative best-spectacle corrected visual acuity (BSCVA) was 0.40 ± 0.21 logMAR which significantly improved to 0.02 ± 0.03 logMAR three months postoperatively (P < 0.001). Mean preoperative IOP was 17.88 ± 2.70 mmHg which significantly decreased to 12.5 ± 1.51 mmHg three months after operation (P < 0.001). Mean duration of operation for these patients was 29.30 ± 8 minutes and mean femtosecond laser process time was 4.20 ± 2 minutes. In terms of complications, 9 patients developed fine subconjunctival hemorrhage and eye redness and 2 patients had mild corneal edema which all subsided within less than 7 days. Serious complications such as anterior or posterior capsule tears were not encountered. Conclusion: Femtosecond laser-assisted cataract surgery is a relatively new method of cataract surgery. Our preliminary results indicate an acceptable visual outcome with no serious complications. However, this technique is lengthier and more expensive than conventional phacoemulsification.
Discovering Important Rules and Loyal Customer by Considering the Repetition in Association Rules

In this paper, we present a study on association rule mining based on real POS data from a SME (small and medium enterprise). Our main contribution is proposing a new criterion, called R-repeat, to evaluate the significance of rules by focusing attention to the purchase pattern repeated by a certain customer. We also focus on detecting a special customer segment, called R-repeater. In addition, we make a comparison between R-repeater segment and the customer segments by REM analysis, and confirm that R-repeater is a loyal customer segment
From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures. ” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.
L'épidémiologie de la dépendance du sujet âgé. Histoire naturelle, tendances évolutives et déterminants

Le contingent de personnes issues de la generation du baby‐boom conjugue a une expansionde la morbidite de plus en plus observee dans certaines couches des populations âgees etvieillissantes constitue un defi majeur pour la societe. L’objectif de cette these etaitd’etudier l’histoire naturelle et les determinants de la dependance dans le vieillissement.Dans une premiere partie nous avons montre que la longevite etait un phenomenemultifactoriel avec des similitudes et des specificites selon le sexe. Dans une deuxiemepartie, l’etude des tendances seculaires a montre un gain d’esperance de vie en 10 ans avecun recul de l’âge d’entree en dependance chez les plus de 75 ans attestant d’unecompression de la morbidite pour les hommes et les hauts niveaux d’etudes, mais d’uneexpansion de la morbidite pour les sujets de bas niveau d’etudes. Les modeles IRT nous ontpermis de decrire plus finement le processus de dependance. Nos travaux ont confirme quela capacite a effectuer les activites quotidiennes etait hierarchiquement affectee et que laporte d’entree dans le processus etait la limitation pour les courses, alors que la derniereetape etait la limitation au transfert. Les trajectoires d’evolution fonctionnelle avant le decessont plus favorables aux hommes et aux hauts niveaux d’etudes. Par ailleurs, les sequenceshierarchiques sont specifiques a la demence, notamment pour les IADL. Les dementsconnaissent un declin fonctionnel plus marque et des niveaux de dependance superieurs.Des analyses ulterieures devront s’attacher a explorer plus largement les differents profilsd’evolution en population âgee et identifier les determinants sous‐jacents a ces profils.
On using mobile crowdsourcing for timely information solicitation and sharing of prices

The increase in goods prices year after year has directly affected consumer expenditures. Survey from a number of countries showed that the average household expenditures had also increased, notably in the past two years. One way to alleviate the impact of the rise in goods prices is by being more selective in buying the items. Being selective means buying the items at the stores that offer the lowest prices. To do that, a mechanism to compare the prices of items between stores is needed, in which, the local pricewatch information solicitation and sharing (LoPrice) model was proposed. The challenge to the design of such model is in the choice of suitable approach for collecting the information on prices from different stores in a timely manner. In this paper, mobile crowdsourcing is proposed to be the approach that is able to address the challenge. Review on existing price comparison applications, including their advantages and disadvantages is also included. An exploratory survey was also performed, which revealed that the use of mobile crowdsourcing is able to provide timely information solicitation and sharing of prices.
A Human Capital Perspective of Organizational Intention to Adopt Open Source Software

This study adopts the human capital perspective as a theoretical lens to understand the factors that influence organizational intention to adopt open source software (OSS). We posit that availability of internal human capital and accessibility to external human capital significantly affect an organization’s intention to adopt OSS either directly or indirectly through switching cost. We analyze survey responses from 81 chief information officers or information systems managers and the results indicate that organizational availability of internal human capital has an indirect effect on an organization’s intention to adopt OSS through switching cost whereas accessibility to external human capital has a direct effect on an organization’s intention to adopt OSS. This study has both theoretical and practical implications. Theoretically, the unique approach from the human capital perspective adds to the extant literature on organizational adoption of innovation and calls for more attention to be directed at understanding the influence of human capital in an increasingly technologically complex environment. Practically, our findings suggest that potential OSS adopters and proponents should develop the human capital necessary for effective exploitation of OSS.
A method of close-range calibration of fisheye lens camera

Fish-eye lens provide ultra-wide field of view, however cause obvious image distortion. The paper provides a close range fisheye lens camera model which reflects the relationship between imaging distortion and object distance. In the model, three camera images shot at various object distances in close range are sufficient to achieve the camera calibration. The experiment demonstrated that the accuracy of image correction was obviously promoted because influence of object distances was well considered, especially in macroshot.
An Energy-aware Routing Mechanism for Latency-sensitive Traffics

With the rapid development of Internet technology and enhanced QoS requirements, network energy consumption has attracted more and more attentions due to the overprovision of network resources. Generally, energy saving can be achieved by sacrificed some performance. However, many popular applications require real-time or soft real-time QoS performance for attracting potential users, and existing technologies can hardly obtain satisfying tradeoffs between energy consumption and performance. In this paper, a novel energy-aware routing mechanism is presented with aiming at reducing the network energy consumption and maintaining satisfying QoS performance for these latency-sensitive applications. The proposed routing mechanism applies stochastic service model to calculate the latency-guarantee for any given network links. Based on such a quantitative latencyguarantee, we further propose a technique to decide whether a link should be powered down and how long it should be kept in power saving mode. Extensive experiments are conducted to evaluate the effectiveness of the proposed mechanism, and the results indicate that it can provide better QoS performance for those latency-sensitive traffics with improved energyefficiency.
Structural Analysis of the 14-3-3ζ/Chibby Interaction Involved in Wnt/β-Catenin Signaling

The partially disordered Chibby (Cby) is a conserved nuclear protein that antagonizes the Wnt/β-catenin signaling pathway. By competing with the Tcf/Lef family proteins for binding to β-catenin, Cby abrogates the β-catenin-mediated transcription of Wnt signaling genes. Additionally, upon phosphorylation on S20 by the kinase Akt, Cby forms a complex with 14-3-3 to facilitate the nuclear export of β-catenin, which represents another crucial mechanism for the regulation of Wnt signaling. To obtain a mechanistic understanding of the 14-3-3/Cby interaction, we have extensively characterized the complex using X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and isothermal titration calorimetry (ITC). The crystal structure of the human 14-3-3ζ/Cby protein-peptide complex reveals a canonical binding mode; however the residue at the +2 position from the phosphorylated serine is shown to be uniquely oriented relative to other solved structures of 14-3-3 complexes. Our ITC results illustrate that although the phosphorylation of S20 is essential for Cby to recognize 14-3-3, residues flanking the phosphorylation site also contribute to the binding affinity. However, as is commonly observed in other 14-3-3/phosphopeptide crystal structures, residues of Cby flanking the 14-3-3 binding motif lack observable electron density. To obtain a more detailed binding interface, we have completed the backbone NMR resonance assignment of 14-3-3ζ. NMR titration experiments reveal that residues outside of the 14-3-3 conserved binding cleft, namely a flexible loop consisting of residues 203-210, are also involved in binding Cby. By using a combined X-ray and NMR approach, we have dissected the molecular basis of the 14-3-3/Cby interaction.
Application Behavior Analysis by Stateful Automata Mechanism

A sufficient visibility into the behaviors of network applications from the Internet traffic is essential to the content security, traffic management, and measurement. This paper presents a methodology to perform a reliable traffic classification and distinguish activities of specific applications. Our approach uses the flow-based state machine to model a given network application and its behaviors (even with the encryption) and combines the signature matching, protocol analysis, and statistical test in order to make use of the strength of the three approaches. We further discuss the system design and the implementation of our framework, including the detection heuristics and system details. These systems are already deployed at the borders of network environments of several enterprises and organizations. At last, we demonstrate the effectiveness of the approach by applying it to identify various applications and malicious traffic. This study on application behaviors shows that it is possible to allow the expected activities of programs but disallow others between the endpoint users.
Feature Selection Algorithm Based on Mutual Information and Lasso for Microarray Data

With the development of microarray technology, massive microarray data is produced by gene expression experiments, and it provides a new approach for the study of human disease. Due to the characteristics of high dimensionality, much noise and data redundancy for microarray data, it is difficult to my knowledge from microarray data profoundly and accurately,and it also brings enormous difficulty for information genes selection. Therefore, a new feature selection algorithm for high dimensional microarray data is proposed in this paper, which mainly involves two steps. In the first step, mutual information method is used to calculate all genes, and according to the mutual information value, information genes is selected as candidate genes subset and irrelevant genes are filtered. In the second step, an improved method based on Lasso is used to select information genes from candidate genes subset, which aims to remove the redundant genes. Experimental results show that the proposed algorithm can select fewer genes, and it has better classification ability, stable performance and strong generalization ability. It is an effective genes feature selection algorithm.
Using evolutionary algorithms for ECG Arrhythmia detection and classification

The electrocardiogram (ECG) is the most clinically accepted diagnostic tool used by physicians for interpreting the functional activity of the heart. The existing ECG machines require an expert-in-the-loop for identifying abnormalities in cardiac activity - commonly referred to as Arrhythmia - of a patient. The accuracy of diagnosis is directly dependent on the skill set of the physician; as a result, in rural and remote places, where no ECG specialist wants to relocate, the patients are unable to get any help in case of life threatening arrhythmias. In this paper, we investigate the suitability of evolutionary algorithms to discriminate a normal ECG from an abnormal one with minimum user intervention. Consequently, the human dependent errors are minimized. The intelligent framework is efficient and can be used for realtime ECG analysis to complement the diagnostic efficiency and accuracy of ECG specialists. Moreover, the system could also be used to raise early alarms for patients where no ECG specialist is available. In this paper, we aim at autonomously detecting six types of Arrhythmia: (1) Tachycardia, (2) Bradycardia, (3) Right Bundle Branch Block, (4) Left Bundle Branch Block, (5) Old Inferior Myocardial Infarction, and (6) Old Anterior Myocardial Infarction. We evaluate the accuracy of our system by selecting the best back end classifier from a set of 8 evolutionary classifiers. The results of our experiments show that our system is able to achieve more than 98% accuracy in detecting most types of Arrhythmia.