Miracle-dz/UniFAR-FTUs huggingface dataset:
An original interpretation of the wet edge of the surface temperature–albedo space to estimate crop evapotranspiration (SEB-1S), and its validation over an irrigated area in northwestern Mexico

The space defined by the pair surface tempera- ture (T ) and surface albedo ( ), and the space defined by the pair T and fractional green vegetation cover (fvg) have been extensively used to estimate evaporative fraction (EF) from solar/thermal remote sensing data. In both space-based approaches, evapotranspiration (ET) is estimated as remotely sensed EF times the available energy. For a given data point in the T space or in the T fvg space, EF is derived as the ratio of the distance separating the point from the line identified as the dry edge to the distance separating the dry edge and the line identified as the wet edge. The dry and wet edges are classically defined as the upper and lower limit of the spaces, respectively. When investigating side by side the T and theT fvg spaces, one observes that the range cov- ered by T values on the (classically determined) wet edge is different for both spaces. In addition, when extending the wet and dry lines of the T space, both lines cross at 0.4 although the wet and dry edges of the T fvg space never cross for 0 fvg < 1. In this paper, a new ET (EF) model (SEB-1S) is derived by revisiting the classical physical in- terpretation of the T space to make its wet edge consis- tent with that of the T fvg space. SEB-1S is tested over a 16 km by 10 km irrigated area in northwestern Mexico dur- ing the 2007-2008 agricultural season. The classical T space-based model is implemented as benchmark to evalu- ate the performance of SEB-1S. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Re- flection radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-1S and the classical T space-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study do- main. The ET simulated by SEB-1S is significantly more ac- curate and robust than that predicted by the classical T space-based model. The correlation coefficient and slope of the linear regression between simulated and observed ET is improved from 0.82 to 0.93, and from 0.63 to 0.90, respec- tively. Moreover, constraining the wet edge using air temper- ature data improves the slope of the linear regression between simulated and observed ET.
The Impact of Message Sidedness on Online Ads Effectivenss: The Moderating Role of Involvement

This study considers the moderating effect of involvement on consumers’ response to one-sided vs. two-sided online advertising. Results obtained from an experimental study confirm that involved consumers’ responses to two-sided online ads are more favorable than that of one-sided online ad, but only for involved consumers. Two-sided ad is no more persuasive for uninvolved consumers except for those who recognize the two-sided nature of the communication. The routes to persuasion of two-sided ads are also different for involved and uninvolved consumers. These findings provide a new economical way for enterprises to improve their online advertising effectiveness in the absence of trust nowadays.
Unsupervised Language Model Adaptation Incorporating Named Entity Information

Language model (LM) adaptation is important for both speech and language processing. It is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, this paper investigates how effectively using named entity (NE) information, instead of considering all the words, helps LM adaptation. We evaluate two latent topic analysis approaches in this paper, namely, clustering and Latent Dirichlet Allocation (LDA). In addition, a new dynamically adapted weighting scheme for topic mixture models is proposed based on LDA topic analysis. Our experimental results show that the NE-driven LM adaptation framework outperforms the baseline generic LM. The best result is obtained using the LDA-based approach by expanding the named entities with syntactically filtered words, together with using a large number of topics, which yields a perplexity reduction of 14.23% compared to the baseline generic LM.
A Strategy to Determine When to Stop Using Automatic Bug Localization

Information retrieval based automatic bug localization techniques provide developers a ranked list of suspicious buggy source entities to aid locate the ones needed to be modified and to fix the bug. However, it is unavoidable that some buggy entities are ranked low in the result list using these automatic techniques. We assume a bug localization process to address this challenge. Each time a source code entity in the ranked list is examined, the developers will have the option as to whether to continue examining the automatic bug localization result, or simply switch to using a conventional localization approach. We propose a new evaluation metric called ETC (Expected Time Cost) in the localization process, which includes the time cost of using the conventional approach. Under our assumptions, we derived simple criteria to minimize ETC. We compared the time cost of a state-of-art automatic localization method, BugLocator, with and without using our strategy in two projects. The result shows that using our proposed strategy combining both automatic localization technique together with conventional approach performs better than using only either the automatic localization technique or the conventional approach.
Computerized Medical Imaging and Graphics

Computer-aided prognosis (CAP) is a new and exciting complement to the ﬁeld of computer-aided diagnosis (CAD) and involves developing and applying computerized image analysis and multi-modal data fusion algorithms to digitized patient data (e.g. imaging, tissue, genomic) for helping physicians predict disease outcome and patient survival. While a number of data channels, ranging from the macro (e.g. MRI) to the nano-scales (proteins, genes) are now being routinely acquired for disease characterization, one of the challenges in predicting patient outcome and treatment response has been in our inability to quantitatively fuse these disparate, heterogeneous data sources. At the Laboratory for Computational Imaging and Bioinformatics (LCIB) 1 at Rutgers University, our team has been developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome from multiple modalities including MRI, digital pathology, and protein expression. Additionally, we have been developing novel data fusion algorithms based on non-linear dimensionality reduction methods (such as Graph Embedding) to quantitatively integrate information from multiple data sources and modalities with the overarching goal of optimizing meta-classiﬁers for making prognostic predictions. In this paper, we brieﬂy describe 4 representative and ongoing CAP projects at LCIB. These projects include (1) an Image-based Risk Score (IbRiS) algorithm for predicting outcome of Estrogen receptor positive breast cancer patients based on quantitative image analysis of digitized breast cancer biopsy specimens alone, (2) segmenting and determining extent of lymphocytic inﬁltration (identiﬁed as a possible prognostic marker for outcome in human epidermal growth factor ampliﬁed breast cancers) from digitized histopathology, (3) distinguishing patients with different Gleason grades of prostate cancer (grade being known to be correlated to outcome) from digitized needle biopsy specimens, and (4) integrating protein expression measurements obtained from mass spectrometry with quantitative image features derived from digitized histopathology for distinguishing between prostate cancer patients at low and high risk of disease recurrence following radical prostatectomy.
Composition and Acidification of the Culture Medium Influences Chronological Aging Similarly in Vineyard and Laboratory Yeast

Chronological aging has been studied extensively in laboratory yeast by culturing cells into stationary phase in synthetic complete medium with 2% glucose as the carbon source. During this process, acidification of the culture medium occurs due to secretion of organic acids, including acetic acid, which limits survival of yeast cells. Dietary restriction or buffering the medium to pH 6 prevents acidification and increases chronological life span. Here we set out to determine whether these effects are specific to laboratory-derived yeast by testing the chronological aging properties of the vineyard yeast strain RM11. Similar to the laboratory strain BY4743 and its haploid derivatives, RM11 and its haploid derivatives displayed increased chronological life span from dietary restriction, buffering the pH of the culture medium, or aging in rich medium. RM11 and BY4743 also displayed generally similar aging and growth characteristics when cultured in a variety of different carbon sources. These data support the idea that mechanisms of chronological aging are similar in both the laboratory and vineyard strains.
The Benzenesulfoamide T0901317 [N-(2,2,2-Trifluoroethyl)-N-[4-[2,2,2-trifluoro-1-hydroxy-1-(trifluoromethyl)ethyl]phenyl]-benzenesulfonamide] Is a Novel Retinoic Acid Receptor-Related Orphan Receptor-α/γ Inverse Agonist

Retinoic acid receptor-related orphan receptors (RORs) regulate a variety of physiological processes including hepatic gluconeogenesis, lipid metabolism, circadian rhythm, and immune function. Here we present the first high-affinity synthetic ligand for both RORα and RORγ. In a screen against all 48 human nuclear receptors, the benzenesulfonamide liver X receptor (LXR) agonist N-(2,2,2-trifluoroethyl)-N-[4-[2,2,2-trifluoro-1-hydroxy-1-(trifluoromethyl)ethyl]phenyl]-benzenesulfonamide (T0901317) inhibited transactivation activity of RORα and RORγ but not RORβ. T0901317 was found to directly bind to RORα and RORγ with high affinity (Ki = 132 and 51 nM, respectively), resulting in the modulation of the receptor's ability to interact with transcriptional cofactor proteins. T0901317 repressed RORα/γ-dependent transactivation of ROR-responsive reporter genes and in HepG2 cells reduced recruitment of steroid receptor coactivator-2 by RORα at an endogenous ROR target gene (G6Pase). Using small interference RNA, we demonstrate that repression of the gluconeogenic enzyme glucose-6-phosphatase in HepG2 cells by T0901317 is ROR-dependent and is not due to the compound's LXR activity. In summary, T0901317 represents a novel chemical probe to examine RORα/γ function and an excellent starting point for the development of ROR selective modulators. More importantly, our results demonstrate that small molecules can be used to target the RORs for therapeutic intervention in metabolic and immune disorders.
Coordinated graph and scatter-plot views for the visual exploration of microarray time-series data

Microarrays are relatively new, high-throughput data acquisition technology for investigating biological phenomena at the micro-level. One of the more common procedures for microarray experimentation is that of the microarray time-course experiment. The product of microarray time-course experiment is time-series data, which subject to proper analysis has the potential to have significant impact on the diagnosis, treatment, and prevention of diseases. While existing information visualization techniques go some way to making microarray time-series data more manageable, requirements analysis has revealed significant limitations. The main finding was that users were unable to uncover and quantify common changes in value over a specified time-period. This paper describes a novel technique that provides this functionality by allowing the user to visually formulate and modify measurable queries with separate time-period and condition components. These visual queries are supported by the combination of a traditional value against time graph representation of the data with a complementary scatter-plot representation of a specified time-period. The multiple views of the visualization are coordinated so that the user can formulate and modify queries with rapid reversible display of query results in the traditional value against time graph format.
Bayesian Nonparametric Collaborative Topic Poisson Factorization for Electronic Health Records-Based Phenotyping

Phenotyping with electronic health records (EHR) has received much attention in recent years because the phenotyping opens a new way to discover clinically meaningful insights, such as disease progression and disease subtypes without human supervisions. In spite of its potential benefits, the complex nature of EHR often requires more sophisticated methodologies compared with traditional methods. Previous works on EHR-based phenotyping utilized unsupervised and supervised learning methods separately by independently detecting phenotypes and predicting medical risk scores. To improve EHR-based phenotyping by bridging the separated methods, we present Bayesian nonparametric collaborative topic Poisson factorization (BN-CTPF) that is the first nonparametric content-based Poisson factorization and first application of jointly analyzing the phenotye topics and estimating the individual risk scores. BN-CTPF shows better performances in predicting the risk scores when we compared the model with previous matrix factorization and topic modeling methods including a Poisson factorization and its collaborative extensions. Also, BN-CTPF provides faceted views on the phenotype topics by patients' demographics. Finally, we demonstrate a scalable stochastic variational inference algorithm by applying BN-CTPF to a national-scale EHR dataset.
CRP2 (C/EBP beta) contains a bipartite regulatory domain that controls transcriptional activation, DNA binding and cell specificity.

Two members of the C/EBP family of basic region‐leucine zipper proteins enriched in the liver, C/EBP (C/EBP alpha) and CRP2 (C/EBP beta), were previously shown to transactivate the albumin promoter in a cell type‐dependent manner. These proteins function efficiently in HepG2 hepatoma cells, but inefficiently in HeLa (epithelial) and L (fibroblastic) cells. Here we have investigated the mechanism for cell‐specific control of CRP2 activity. We show that CRP2 contains a negative regulatory region composed of two elements, RD1 and RD2. Deletions of RD2 relieve the inhibition of CRP2 activity in L cells, but do not affect CRP2 function in HepG2 cells. These deletions also increase the DNA binding activity of CRP2 approximately 3‐fold, suggesting that RD2‐mediated repression of DNA binding activity is responsible for CRP2 inhibition in L cells. The adjacent RD1 element functions independently of RD2 and modulates the CRP2 activation domain, which we show to be composed of three subdomains that are conserved within the C/EBP protein family. RD1 does not affect cell type specificity, but inhibits the transactivation potential of GAL4‐CRP2 hybrid proteins by 50‐fold. These findings suggest that CRP2 assumes a tightly folded conformation in which the DNA binding and activation domains are masked by interactions with the regulatory domain and that to function efficiently in HepG2 cells the protein must undergo an activation step. We propose that relief of inhibition conferred by the regulatory domains also accounts for CRP2 activation in response to extracellular signals.
Analysis of IBS for MANET Security in Emergency and Rescue Operations

Protection of the network layer in mobile ad hoc networks (MANETs) imply cryptographically signed routing messages. Identity-based signature (IBS) schemes make bandwidth consuming certificate exchanges obsolete. User identifiers serve as public keys. However long-term identifiers are required. This paper analyzes the applicability of IRS schemes for securing routing information in MANETs for emergency and rescue operations. The optimized link state routing protocol (OLSR) serves as example
A geminivirus replication protein interacts with the retinoblastoma protein through a novel domain to determine symptoms and tissue specificity of infection in plants

Geminiviruses replicate in nuclei of mature plant cells after inducing the accumulation of host DNA replication machinery. Earlier studies showed that the viral replication factor, AL1, is sufficient for host induction and interacts with the cell cycle regulator, retinoblastoma (pRb). Unlike other DNA virus proteins, AL1 does not contain the pRb binding consensus, LXCXE, and interacts with plant pRb homo logues (pRBR) through a novel amino acid sequence. We mapped the pRBR binding domain of AL1 between amino acids 101 and 180 and identified two mutants that are differentially impacted for AL1–pRBR interactions. Plants infected with the E‐N140 mutant, which is wild‐type for pRBR binding, developed wild‐type symptoms and accumulated viral DNA and AL1 protein in epidermal, mesophyll and vascular cells of mature leaves. Plants inoculated with the KEE146 mutant, which retains 16% pRBR binding activity, only developed chlorosis along the veins, and viral DNA, AL1 protein and the host DNA synthesis factor, proliferating cell nuclear antigen, were localized to vascular tissue. These results established the importance of AL1–pRBR interactions during geminivirus infection of plants.
From information management to task management in electronic mail

Electronic mail (e-mail) is an under-utilised resource of information and knowledge. It could be an important part of the larger so-called organisational memory (OM)—if it were not so disorganised and fragmented. The OM contains the knowledge of the organisation’s employees, written records, and data. This thesis is about organising and managing information in, and about, e-mail so as to make it retrievable and usable for task management purposes. The approach is user-centred and based on a conceptual model for task management. The model is designed to handle tasks that occur in the communications in an open distributed system, such as Internet e-mail. Both structured and unstructured tasks can be supported. Furthermore, the model includes management of desktop source information, which comprises the different electronically available sources in a user’s computer environment. The information from these is used in the model to sort information and thereby handle tasks and related information. Tasks are managed as conversations, that is, exchanges of messages. We present a language called Formal Language for Conversations (FLC), based on speech act theory, which is used to organise messages and relevant information for tasks. FLC provides the container for task-related information, as well as the context for managing tasks. The use of FLC is exemplified in two scenarios: scheduling a meeting and making conference arrangements. We describe a prototype based on the conceptual model. The prototype explicitly refines and supports the notion of threads, which are employed so as to give tasks a context. It integrates the use of FLC into the traditional threading mechanism of e-mail, in addition to matching on text in the body. An agent architecture is also described, which is used to harmonise the information in the heterogeneous desktop sources. Finally, human-readable filtering rules created by a machine learning algorithm are employed in the prototype. The prototype is evaluated with regard to its thread-matching capability, as well as the creation of usable and readable filtering rules. Both are deemed satisfactory.
P2P systems in legal networks: another

The "small world"-paradigm offers a new interesting view-point for the analysis of contemporary legal networks and artificial intelligence. This topological approach sheds further light on such different fields as case-based legal reasoning, knowledge discovery in legal databases, or legal ontologies, as far as clustering coefficients, diameter and hubs of the network are involved. Moreover, empirical evidence shows that even P2P systems as Gnutella present small world-features. So it becomes possible to deepen our understanding of how spontaneous communities organize themselves in the network. While opening new horizons in the field of recommender systems, it also widens our perspective in dealing with such important issues as privacy and digital copyright.
RICK, a Novel Protein Kinase Containing a Caspase Recruitment Domain, Interacts with CLARP and Regulates CD95-mediated Apoptosis*

Signaling through the CD95/Fas/APO-1 death receptor plays a critical role in the homeostasis of the immune system. RICK, a novel protein kinase that regulates CD95-mediated apoptosis was identified and characterized. RICK is composed of an N-terminal serine-threonine kinase catalytic domain and a C-terminal region containing a caspase-recruitment domain. RICK physically interacts with CLARP, a caspase-like molecule known to bind to Fas-associated protein with death domain (FADD) and caspase-8. Expression of RICK promoted the activation of caspase-8 and potentiated apoptosis induced by Fas ligand, FADD, CLARP, and caspase-8. Deletion mutant analysis revealed that both the kinase domain and caspase-recruitment domain were required for RICK to promote apoptosis. Significantly, expression of a RICK mutant in which the lysine of the putative ATP-binding site at position 38 was replaced by a methionine functioned as an inhibitor of CD95-mediated apoptosis. Thus, RICK represents a novel kinase that may regulate apoptosis induced by the CD95/Fas receptor pathway.
Reliable communication in multihop networks despite byzantine failures. (Communication fiable dans les réseaux multi-sauts en présence de fautes byzantines)

As modern networks grow larger and larger, they become more likely to fail. Indeed, their nodes can be subject to attacks, failures, memory corruptions. In order to encompass all possible types of failures, we consider the most general model of failure: the Byzantine model, where the failing nodes have an arbitrary (and thus, potentially malicious) behavior. Such failures are extremely dangerous, as one single Byzantine node, if not neutralized, can potentially lie to the entire network.We consider the problem of reliably exchanging information in a multihop network despite such Byzantine failures. Solutions exist but require a dense network, where each node has a large number of neighbors. In this thesis, we propose solutions for sparse networks, such as the grid, where each node has at most 4 neighbors.In a first part, we accept that some correct nodes fail to communicate reliably. In exchange, we propose quantitative solutions that tolerate a large number of Byzantine failures, and significantly outperform previous solutions in sparse networks. In a second part, we propose algorithms that ensure reliable communication between all correct nodes, provided that the Byzantine nodes are sufficiently distant from each other. At last, we generalize existing results to new contexts: dynamic networks, and networks with an unbounded diameter.
A Study of Ultrawideband Antennas for Near-Field Imaging

A compact ultrawideband (UWB) antenna has been designed for UWB cancer detection systems. The numerical analysis and experimental characterization of a printed monopole antenna fed by a 50-Omega coplanar waveguide (CPW) and operating in a lossy coupling medium is presented. A detailed 3D model of the antenna and the biological environment has been constructed and simulated in XFDTD and HFSS. The near-field pattern of the antenna in the proximity of a multilayer human body model and the effect of the human body on the input matching of the antenna are studied. Experimental results show that the return loss is below -9.6 dB from 3.4 GHz to 9.9 GHz