Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of transforming growth factor-beta in transdifferentiation and fibrosis of lens epithelial cells.

PURPOSE To determine the levels of mRNAs encoding markers of fibrosis in lens epithelial cells (LECs) from patients with anterior polar cataracts and to test whether transforming growth factor (TGF)-beta enhances the expression of mRNAs for mesenchymal markers in LECs. METHODS LECs attached to the anterior capsules of patients with nuclear or anterior polar cataracts were analyzed by reverse transcription-polymerase chain reaction (RT-PCR) for the expression of mRNAs encoding pathologic extracellular matrix proteins, a marker of myofibroblast transformation, growth factors, and growth factor receptors, and by western blot analysis for the proteins encoded by these mRNAs. Bovine lens epithelial explants and intact rabbit lenses cultured with or without TGF-beta1 were also subjected to RT-PCR and western blot analysis. RESULTS The levels of fibronectin, type I collagen, and alpha-smooth muscle actin (SMA) mRNAs were higher in LECs from patients with anterior polar cataracts than in those from patients with nuclear cataracts. Expression of mRNAs for TGF-beta1, TGF-beta2, TGF-beta receptor type II, and connective tissue growth factor (CTGF) was significantly greater in anterior polar type than in nuclear type cataracts. In contrast, expression of mRNAs for epidermal growth factor (EGF), epidermal growth factor receptor (EGFR), fibroblast growth factor (FGF)-2, and FGF receptor-1 was similar in LECs from the two types of cataracts. TGF-beta1 markedly increased the levels of fibronectin, type I collagen, and alpha-SMA mRNA in bovine lens epithelial explants and intact rabbit lenses. CONCLUSIONS This is the first finding showing altered mRNA expression in LECs from anterior polar cataracts. Enhanced expression of TGF-beta and the TGF-beta receptor suggests that TGF-beta derived from LECs may function in an autocrine fashion as the prime mediator of transdifferentiation and pathogenesis in human LECs. Elevated levels of CTGF mRNA suggest that this growth factor may play a role in the increased deposition of extracellular matrix in metaplastic LECs.
Instant Espresso: Interactive Analysis of Relationships in Knowledge Graphs

We demonstrate InstantEspresso, a system to explain the relationship between two sets of entities in knowledge graphs. Instant-Espresso answers questions of the form. Which European politicians are related to politicians in the United States, and how? or How can one summarize the relationship between China and countries from the Middle East? Each question is specified by two sets of query entities. These sets (e.g. European politicians or United States politicians) can be determined by an initial graph query over a knowledge graph capturing relationships between real-world entities. Instant-Espresso analyzes the (indirect) relationships that connect entities from both sets and provides a user-friendly explanation of the answer in the form of concise subgraphs. These so-called relatedness cores correspond to important event complexes involving entities from the two sets. Our system provides a user interface for the specification of entity sets and displays a visually appealing visualization of the extracted subgraph to the user. The demonstrated system can be used to provide background information on the current state-of-affairs between real-world entities such as politicians, organizations, and the like, e.g. to a journalist preparing an article involving the entities of interest. InstantEspresso is available for an online demonstration at the URL http://espresso.mpi-inf.mpg. de/.
Multi-Objective Optimization for Hard Real-World Problems under Limited Budgets

Sequential parameter optimization (SPO) is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms and optimization tasks. Since only a fraction of the interesting industrial Problems are single objective it is fundamental to provide algorithms with the SPO-Toolbox (SPOT) to optimize multi objective problems. Moreover, with the resulting capability to allow competing objectives, the opportunity arises to not only aim for the best, but also for the most robust solution. In this Paper we show that multi objective optimization can be e ciently done with SPOT. Furthermore we present an approach to optimize not only the quality of the solution, but also its robustness, taken these two goals as criteria for multi objective optimization. The results that we are presenting show.
General Indexation of Weighted Automata - Application to Spoken Utterance Retrieval

Much of the massive quantities of digitized data widely available, e.g., text, speech, hand-written sequences, are either given directly, or, as a result of some prior processing, as weighted automata. These are compact representations of a large number of alternative sequences and their weights reflecting the uncertainty or variability of the data. Thus, the indexation of such data requires indexing weighted automata. We present a general algorithm for the indexation of weighted automata. The resulting index is represented by a deterministic weighted transducer that is optimal for search: the search for an input string takes time linear in the sum of the size of that string and the number of indices of the weighted automata where it appears. We also introduce a general framework based on weighted transducers that generalizes this indexation to enable the search for more complex patterns including syntactic information or for different types of sequences, e.g., word sequences instead of phonemic sequences. The use of this framework is illustrated with several examples. We applied our general indexation algorithm and framework to the problem of indexation of speech utterances and report the results of our experiments in several tasks demonstrating that our techniques yield comparable results to previous methods, while providing greater generality, including the possibility of searching for arbitrary patterns represented by weighted automata.
Ontology learning from text: Evaluation of learned ontologies

Ontologies provide a structural organizational knowledge, they support the exchange and sharing of information. Moreover, one of the main benefits of using ontologies is the ability to infer new knowledge that allows the development of more realistic applications such as semantic search, automated reasoning, classification, query answering, among others. The need for overcoming the bottleneck, given in the knowledge acquisition by the manual construction of ontologies, has motivated studies on semi-automatic and automatic methods to build ontologies. One of the main sources of knowledge created by humans is given by text resources. The analysis and extraction of the elements of an ontology from texts is a very hard task. In particular, the acquisition of more expressive elements is our main goal. In this report we focus on present the experiments on four corpora and the evaluation made of the identified axiomatic relations (subClassOf, disjointWith, and equivalentClass). Also, we include the results compared with two related works showing prominent results. This report of activities corresponds to the fourth year of doctoral studies.
LWB and FS-LWB implementation for Sky platform using Contiki

The low-power wireless bus (LWB) is a highly efficient communication protocol for wireless sensor networks (WSN). However, a lack of open implementation for this protocol prompted the implementation of the protocol for the most common platform of WSN related research, the sky nodes using the Contiki operating system. This document provides the detailed description of the implementation. A forwarder-selection mechanism is developed to improve energy-efficiency of LWB in data collection scenario. The implementation also consists the forwarder selection mechanism. One can either use the original features of LWB or enforce the forwarder selection mechanism.
K INVENTORY MODELS WITH RAMP-TYPE DEMAND FOR DETERIORATING ITEMS WITH PARTIAL BACKLOGGING AND TIME-VARING HOLDING COST Biplab KARMAKAR

In this paper, an inventory model with general ramp-type demand rate, partial backlogging of unsatisfied demand and time-varying holding cost is considered; where the “Time-varying holding cost” means that the holding cost is a function of time, i.e. it is time dependent. The model is studied under the following different replenishment policies: (1) starting with no shortages (2) starting with shortages. Two component demand rate has been used. The backlogging rate is any non-increasing function of the waiting time up to the next replenishment. The optimal replenishment policy is derived for both the above mentioned policies.
Feature Selection via Discretization

Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant and/or redundant attributes. Chi2 is a simple and general algorithm that uses the /spl chi//sup 2/ statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data. It achieves feature selection via discretization. It can handle mixed attributes, work with multiclass data, and remove irrelevant and redundant attributes.
Research on the Framework of Information Visualization System of Equipment Support Decision-Making

The paper elaborates the concept and connotation of information visualization of equipment support. On the basis of visualization principle, it puts forward information visualization system structure of equipment support decision-making according to the characteristics of equipment support decision-making. With visualization of equipment maintenance in a work unit as a living example, it finally illustrates information visualization of equipment support decision-making from two perspectives -- scientific visualization and data visualization.
An Equilibrated A Posteriori Error Estimator for the Interior Penalty Discontinuous Galerkin Method

Interior penalty discontinuous Galerkin (IPDG) methods for second order elliptic boundary value problems have been derived from a mixed variational formulation of the problem. Numerical flux functions across interelement boundaries play an important role in that theory. Residual type a posteriori error estimators for IPDG methods have been derived and analyzed by many authors including the convergence analysis of the resulting adaptive schemes. Typically, the effectivity indices deteriorate with increasing polynomial order of the IPDG methods. The situation is more favorable for a posteriori error estimators derived by means of the so-called hypercircle method. Equilibrated fluxes are obtained by using a mixed method and an extension operator for Brezzi--Douglas--Marini elements, and this can be done in the same way for all the discontinuous Galerkin methods that fit into a known unified framework. The hypercircle method immediately provides the reliability of the estimator, whereas its efficiency can be .
Coordinated and Interactive Expression of Genes of Lipid Metabolism and Inflammation in Adipose Tissue and Liver during Metabolic Overload

Background Chronic metabolic overload results in lipid accumulation and subsequent inflammation in white adipose tissue (WAT), often accompanied by non-alcoholic fatty liver disease (NAFLD). In response to metabolic overload, the expression of genes involved in lipid metabolism and inflammatory processes is adapted. However, it still remains unknown how these adaptations in gene expression in expanding WAT and liver are orchestrated and whether they are interrelated. Methodology/Principal Findings ApoE*3Leiden mice were fed HFD or chow for different periods up to 12 weeks. Gene expression in WAT and liver over time was evaluated by micro-array analysis. WAT hypertrophy and inflammation were analyzed histologically. Bayesian hierarchical cluster analysis of dynamic WAT gene expression identified groups of genes (‘clusters’) with comparable expression patterns over time. HFD evoked an immediate response of five clusters of ‘lipid metabolism’ genes in WAT, which did not further change thereafter. At a later time point (>6 weeks), inflammatory clusters were induced. Promoter analysis of clustered genes resulted in specific key regulators which may orchestrate the metabolic and inflammatory responses in WAT. Some master regulators played a dual role in control of metabolism and inflammation. When WAT inflammation developed (>6 weeks), genes of lipid metabolism and inflammation were also affected in corresponding livers. These hepatic gene expression changes and the underlying transcriptional responses in particular, were remarkably similar to those detected in WAT. Conclusion In WAT, metabolic overload induced an immediate, stable response on clusters of lipid metabolism genes and induced inflammatory genes later in time. Both processes may be controlled and interlinked by specific transcriptional regulators. When WAT inflammation began, the hepatic response to HFD resembled that in WAT. In all, WAT and liver respond to metabolic overload by adaptations in expression of gene clusters that control lipid metabolism and inflammatory processes in an orchestrated and interrelated manner.
GNAED: A data mining framework for network-wide abnormal event detection in backbone networks

In backbone networks, due to the frequent exchange of data between adjacent routers, the characteristics of network behaviors caused by network distributed abnormal events are closely related with routers' spatial location, their connection relationships and other associated information. Based on this observation, this paper presents a novel graph-based abnormal event detection framework that uses a set of data mining techniques for facilitating the detection of distributed abnormal events in backbone networks. The experiment results using real traffic data collected from an ISP backbone network show the effective and scalable of the proposed framework.
CreaDO -- A Methodology to Create Domain Ontologies Using Parameter-Based Ontology Merging Techniques

Nowadays, ontologies have become a key mechanism to represent the knowledge of a specific domain. Domain ontologies can be used for different purposes, one of them is the development of semantic search engines that obtain precise results by considering the meaning of the Web content. The construction of these ontologies usually requires a large amount of effort and time to be completed. One way of reducing such effort and time is using the reuse approach. When ontologies overlap, alignment techniques to merge such ontologies can be applied. However, the result of the ontology merging activity can be a very large ontology, difficult to understand and use. To overcome this problem, this paper describes a methodology, called CreaDO, to semi-automatically create domain ontologies. CreaDO focuses on performing a parameter-based ontology merge that allows the creation of a domain ontology only with relevant information for the ontology purpose.
The Look of Tiredness: Evaluation of Pilot Fatigue Based on Video Recordings

The present study explores the usefulness of a method to evaluate pilot fatigue based on video recordings. This technique is already being successfully applied in the field of driver fatigue evaluations. Still, to date, there is no known application concerning pilot fatigue. A sample of five trained raters evaluated four active airline pilots on simulated flights using a 9-point fatigue rating scale. The rating scale was adjusted for the aviation context and features 10 subscales relating to different indicators for fatigue. Interrater agreement and reliability, as well as intrarater and retest reliability, were assessed. The results indicate good reliability of the method and medium rater agreement. Raters seem to have established a good mental model of fatigue, leading to consistent evaluations. However, there are also individual rating tendencies as well as the influence of sample characteristics.
Complexity Results in Epistemic Planning

Epistemic planning is a very expressive framework that extends automated planning by the incorporation of dynamic epistemic logic (DEL). We provide complexity results on the plan existence problem for multi-agent planning tasks, focusing on purely epistemic actions with propositional preconditions. We show that moving from epistemic preconditions to propositional preconditions makes it decidable, more precisely in EXPSPACE. The plan existence problem is PSPACE-complete when the underlying graphs are trees and NP-complete when they are chains (including singletons). We also show PSPACE-hardness of the plan verification problem, which strengthens previous results on the complexity of DEL model checking.
Modelling High Frequency Signal Propagation Over Low Voltage Overhead Distribution Networks Using EMTP

This paper examines fundamentals of signal propagation over low-voltage (LV) bare conductor power lines. It is well known that power line carrier (PLC) signals are highly attenuated by load impedances. In this paper high frequency propagation over distribution lines is modeled using the electromagnetic power system transients program (EMTP). A sample distribution system is analysed for different load types and magnitudes. In this model frequency-dependent parameters of lines are included such as skin effect and ground coupling. It is found that loads may significantly attenuate high frequency signals on LV lines and a novel technique is proposed to improve the high-frequency signal propagation characteristics
The use of a mobile sink for quality data collection in energy harvesting sensor networks

In this paper we study data collection in an energy harvesting sensor network where sensors are deployed along a given path and a mobile sink travels along the path periodically for data collection. Such a typical application scenario is to employ a mobile vehicle for traffic surveillance of a given highway. As the sensors in this network are powered by renewable energy sources, the time-varying characteristics of energy harvesting poses great challenges on the design of efficient routing protocols for data collection in harvesting sensor networks. In this paper we first formulate a novel optimization problem as a network utility maximization problem, by incorporating multi-rate communication mechanism between sensors and the mobile sink and show the NP-hardness of the problem. We then devise a novel centralized algorithm for it, assuming that the global knowledge of the entire network is available. We also develop a distributed solution to the problem without the global knowledge assumption. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms are promising and very efficient.
Additive Effects of MicroRNAs and Transcription Factors on CCL2 Production in Human White Adipose Tissue

Adipose tissue inflammation is present in insulin-resistant conditions. We recently proposed a network of microRNAs (miRNAs) and transcription factors (TFs) regulating the production of the proinflammatory chemokine (C-C motif) ligand-2 (CCL2) in adipose tissue. We presently extended and further validated this network and investigated if the circuits controlling CCL2 can interact in human adipocytes and macrophages. The updated subnetwork predicted that miR-126/-193b/-92a control CCL2 production by several TFs, including v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) (ETS1), MYC-associated factor X (MAX), and specificity protein 12 (SP1). This was confirmed in human adipocytes by the observation that gene silencing of ETS1, MAX, or SP1 attenuated CCL2 production. Combined gene silencing of ETS1 and MAX resulted in an additive reduction in CCL2 production. Moreover, overexpression of miR-126/-193b/-92a in different pairwise combinations reduced CCL2 secretion more efficiently than either miRNA alone. However, although effects on CCL2 secretion by co-overexpression of miR-92a/-193b and miR-92a/-126 were additive in adipocytes, the combination of miR-126/-193b was primarily additive in macrophages. Signals for miR-92a and -193b converged on the nuclear factor-κB pathway. In conclusion, TF and miRNA-mediated regulation of CCL2 production is additive and partly relayed by cell-specific networks in human adipose tissue that may be important for the development of insulin resistance/type 2 diabetes.
Chapter 29 Social Inequality , Crime , and Deviance

This chapter examines the role of social inequality in crime and deviance by specifying a social psychological theory of the causal mechanisms by which inequality is associated with crime. We begin by noting that the powerful have more input into the content of criminal law, a point illustrated by the relatively soft penalties for white collar and corporate crimes compared to the harsh penalties for street crimes typically committed by the less powerful. We then draw on pragmatist social thought and criminological theory to provide an integrated social psychological explanation that helps explain how social inequality may produce high rates of crime. We apply this perspective to explain crime rates across neighborhoods and communities, as well as crime across the life course. We end with a discussion of the consequences of mass incarceration for reproducing social inequality in the United States.
Current Approaches and Challenges to Development of an Individualized Sleep and Performance Prediction Model

Workplace safety and productivity will be enhanced considerably with the development and application of in- dividualized sleep and performance prediction models. These are models that predict an individual's operational perform- ance based on his/her unique sleep schedule, individual sleep requirements, and individual pattern of responses to sleep loss across a variety of cognitive performance domains. Progress in the individualization of such models will occur as the result of integrated efforts based on (a) an expanding understanding of the relevant physiological processes underlying the sleep/circadian/performance interactions as well as (b) novel empirical and statistical approaches. In the present paper, an overview is presented of the state of the art of the individualization of sleep and performance models with sections on cur- rent efforts in model integration, the application of Bayesian forecasting techniques to the problem of model individuali- zation, construction of Bayesian confidence intervals for predicted performance, and the problem of generalizability of in- dividualized model predictions - i.e., the problem of using models constructed with performance data from one cognitive domain to predict performance in another cognitive domain. Success in model individualization will ultimately be facili- tated by concerted, coordinated efforts involving multiple scientific entities and stakeholders.
FCKPT:A fine-grained incremental checkpoint for PCM

System failures will become more frequent for future large scale systems and issues of fault-tolerance will be tremendously important. Checkpoint-restart techniques are very important fault-tolerance approaches for these systems. As we know, the overhead of checkpoint will influence the system performance seriously. Recently, the emerging Phase Change Memory technology is becoming available and can replace disks with the fast accesses and byte-address features. However, previous research has demonstrated that using conventional incremental checkpointing techniques directly on PCM doesn't perform well. In this paper, we propose and implement a new incremental checkpoint scheme named FCKPT, which can detect exact modified bytes of a dirty page. Our experiment results on several well-known benchmarks show that FCKPT can reduce checkpoint file size from 20%–50% without other overhead when compared to conventional page-level incremental checkpoint on PCM.