Miracle-dz/UniFAR-FTUs huggingface dataset:
Glucocorticoid-binding components in an irradiation-induced thymoma of the C57BL-6J mouse.

Preliminary experiments on thymocyte suspensions derived from normal thymus and γ-irradiation-induced thymomas of the C57BL/6J mouse indicated the presence of macromolecular species capable of binding triamcinolone acetonide-3H specifically with high affinity. Retention of labeled steroid by isolated tumor cells varied considerably among thymomas. Using cell-free preparations and a dextran-coated charcoal procedure, tumor cytosols bound triamcinolone acetonide-3H with a dissociation constant of 3 × 10-9 m; the number of binding sites was 354 fmoles/mg cytosol protein. Cytosols contained binding components that sedimented at 7 to 8 S on low-salt gradients and at 4 to 5 S on gradients containing 0.4 m KCl. These cytoplasmic components bound principally hormones with glucocorticoid activity as indicated by the competition of triamcinolone acetonide-3H binding in the presence of 5 × 10-5 m cortisol, dexamethasone, and cortexolone as well as by progesterone and aldosterone. Neither estradiol-17β, estriol, testosterone, nor dihydrotestosterone at 5 × 10-5 m inhibited specific binding of triamcinolone acetonide-3H. Generally, the binding properties of isolated thymocytes were similar to those determined on cell-free preparations.
Encrypted body-to-body wireless sensor node employing channel-state-based key generation

In this paper, experimental results are presented for an encrypted wireless link between two body-worn sensor nodes fully integrated onto textile antenna platforms. Wireless communication between two persons walking in an indoor environment occurs over a unique physical propagation channel. For the purpose of data encryption, channel measurements are performed at both sides of the link, providing highly correlated pseudo random number sequences, with envelope correlation coefficients always above 0.7. Signals captured by an eavesdropper are substantially decorrelated, with correlation coefficients always below 0.15, and are therefore not suitable for key extraction. The mutual information is also determined, providing extra insight into the key generating potential of the pseudo random sequences. Practical error free secret key generation on both sides of the link, with reconciliation by means of Hamming check bits, is performed successfully in all cases between legitimate parties. At the same time, an eavesdropper is unable to extract the secret key from measured signals, as confirmed by the key error rates, which are calculated for all possible signal combinations, resulting in a key error rate after reconciliation around 0.5 for most eavesdropper channels and remaining at least 0.3 in the worst-case scenario.
Sequential diagnosis of high cardinality faults in knowledge-bases by direct diagnosis generation

Sequential diagnosis methods compute a series of queries for discriminating between diagnoses. Queries are answered by probing such that eventually the set of faults is identified. The computation of queries is based on the generation of a set of most probable diagnoses. However, in diagnosis problem instances where the number of minimal diagnoses and their cardinality is high, even the generation of a set of minimum cardinality diagnoses is unfeasible with the standard conflict-based approach. In this paper we propose to base sequential diagnosis on the computation of some set of minimal diagnoses using the direct diagnosis method, which requires less consistency checks to find a minimal diagnosis than the standard approach. We study the application of this direct method to high cardinality faults in knowledge-bases. In particular, our evaluation shows that the direct method results in almost the same number of queries for cases when the standard approach is applicable. However, for the cases when the standard approach is not applicable, sequential diagnosis based on the direct method is able to locate the faults correctly.
COSIMail, COSIFile: Semantic Desktop Extensions for Email and File Management

In this poster, we present COSIFile and COSIMail, semantic desktop tools for enhanced file and email management that are based on the X-COSIM semantic desktop framework. They are implemented as extensions for an email client and file manager, specifically designed to enhance support for the personal information managment tasks of information organization and re-finding.
Thesis for the Degree of Doctor of Philosophy Improving Community Detection Methods for Network Data Analysis

Empirical analysis of network data has been widely conducted for understanding and predicting the structure and function of real systems and identifying interesting patterns and anomalies. One of the most widely studied structural properties of networks is their community structure. In this thesis we investigate some of the challenges and applications of community detection for analysis of network data and propose different approaches for improving community detection methods. One of the challenges in using community detection for network data analysis is that there is no consensus on a definition for a community despite excessive studies which have been performed on the community structure of real networks. Therefore, evaluating the quality of the communities identified by different community detection algorithms is problematic. In this thesis, we perform an empirical comparison and evaluation of the quality of the communities identified by a variety of community detection algorithms which use different definitions for communities for different applications of network data analysis. Another challenge in using community detection for analysis of network data is the scalability of the existing algorithms. Parallelizing community detection algorithms is one way to improve the scalability of community detection. Local community detection algorithms are by nature suitable for parallelization. One of the most successful approaches to local community detection is local expansion of seed nodes into overlapping communities. However, the communities identified by a local algorithm might cover only a subset of the nodes in a network if the seeds are not selected carefully. The selection of good seeds that are well distributed over a network using only the local structure of a network is therefore crucial. In this thesis, we propose a novel local seeding algorithm, which is based on link prediction and graph coloring, for selecting good seeds for local community detection in large-scale networks. Overall, mining network data has many applications. The focus of this thesis is on analyzing network data obtained from backbone Internet traffic, social networks, and search query log files. We show that mining the structural and temporal properties of email networks generated from Internet backbone traffic can be used to identify unsolicited email from the mixture of email traffic. We also show that a link based community detection algorithm can separate legitimate and unsolicited email into distinct communities. Moreover, we show that, in contrast to previous studies, community detection algorithms can be used for network anomaly detection. We also propose a method for enhancing community detection algorithms and present a framework for using community detection as a basis for network misbehavior detection. Finally, we show that network analysis of query log files obtained from a health care portal can complement the existing methods for semantic analysis of health related queries.
Tidally Driven Export of Dissolved Organic Carbon, Total Mercury, and Methylmercury from a Mangrove-Dominated Estuary

The flux of dissolved organic carbon (DOC) from mangrove swamps accounts for 10% of the global terrestrial flux of DOC to coastal oceans. Recent findings of high concentrations of mercury (Hg) and methylmercury (MeHg) in mangroves, in conjunction with the common co-occurrence of DOC and Hg species, have raised concerns that mercury fluxes may also be large. We used a novel approach to estimate export of DOC, Hg, and MeHg to coastal waters from a mangrove-dominated estuary in Everglades National Park (Florida, USA). Using in situ measurements of fluorescent dissolved organic matter as a proxy for DOC, filtered total Hg, and filtered MeHg, we estimated the DOC yield to be 180 (±12.6) g C m–2 yr–1, which is in the range of previously reported values. Although Hg and MeHg yields from tidal mangrove swamps have not been previously measured, our estimated yields of Hg species (28 ± 4.5 μg total Hg m–2 yr–1 and 3.1 ± 0.4 μg methyl Hg m–2 yr–1) were five times greater than is typically reported for terrestrial wetlands. These results indicate that in addition to the well documented contributions of DOC, tidally driven export from mangroves represents a significant potential source of Hg and MeHg to nearby coastal waters.
Discrimination of Whiskey kimberlite eruptive phases utilizing portable XRF spectrometry data

The purpose of this study was to examine the viability of using a hand-held portable X-ray fluorescence (pXRF) spectrometer to rapidly obtain geochemical data on kimberlite samples, and thus enable discrimination of different kimberlite phases. Potential applications range from augmenting core logging at the exploration stage, to ore grade control during mining. The use of whole rock geochemistry to distinguish different phases of kimberlite within an individual body is well established (e.g. Star kimberlite: Grunsky and Kjarsgaard, 2008; Orion South kimberlite: Kjarsgaard et al., 2009). The samples for this study are from the Whiskey kimberlite, located in the Attawapiskat field, James Bay Lowlands, northern Ontario, Canada (Januszczak et al., 2013).
“On-Off” Thermoresponsive Coating Agent Containing Salicylic Acid Applied to Maize Seeds for Chilling Tolerance

Chilling stress is an important constraint for maize seed establishment in the field. In this study, a type of “on-off” thermoresponsive coating agent containing poly (N-isopropylacrylamide-co-butylmethacrylate) (Abbr. P(NIPAm-co-BMA)) hydrogel was developed to improve the chilling tolerance of coated maize seed. The P(NIPAm-co-BMA) hydrogel was synthesized by free-radical polymerization of N-isopropylacrylamide (NIPAm) and butylmethacrylate (BMA). Salicylic acid (SA) was loaded in the hydrogel as the chilling resistance agent. SA-loaded P(NIPAm-co-BMA) was used for seed film-coating of two maize varieties, Huang C (HC, chilling-tolerant) and Mo17 (chilling-sensitive), to investigate the coated seed germination and seedling growth status under chilling stress. The results showed that the hydrogel obtained a phase transition temperature near 12°C with a NIPAM to MBA weight ratio of 1: 0.1988 (w/w). The temperature of 12°C was considered the “on-off” temperature for chilling-resistant agent release; the SA was released from the hydrogel more rapidly at external temperatures below 12°C than above 12°C. In addition, when seedlings of both maize varieties suffered a short chilling stress (5°C), higher concentrations of SA-loaded hydrogel resulted in increased germination energy, germination percentage, germination index, root length, shoot height, dry weight of roots and shoots and protective enzyme activities and a decreased malondialdehyde content in coated maize seeds compared to single SA treatments. The majority of these physiological and biochemical parameters achieved significant levels compared with the control. Therefore, SA-loaded P(NIPAm-co-BMA), a nontoxic thermoresponsive hydrogel, can be used as an effective material for chilling tolerance in film-coated maize seeds.
Access to general practitioner services: the disabled elderly lag behind in underserved areas.

BACKGROUND Several studies have shown that people living in areas underserved in physicians have reduced odds of consulting. However, beyond the magnitude of this effect averaged for the whole population, policymakers need to know whether specific subgroups faced with transportation difficulties, such as the elderly and especially the disabled elderly, have a particularly restricted access to physicians when residing in underserved areas. METHODS The study sample, representative of the French population aged 18-75 in 1999, comprised 12 405 individuals. Multilevel Poisson models were used to investigate the impact of the area-level density of general practitioners (GPs) on the number of GP consultations reported over the previous 12 months. RESULTS The mean number of GP consultations over the previous 12 months was 3.8 (S.D.=4.9). Multivariate analyses indicated that living in areas underserved in GPs lead to a greater reduction in primary care utilization for the elderly, and especially for the disabled elderly, than for younger age groups. The disabled elderly had 244% more GP consultations (95% CI:+79%, +562%) when they lived in areas with high versus low GP density (defined with the 10th and 90th percentiles as cut-offs). CONCLUSION If further research confirms our findings, this increasingly disturbing public health issue in industrialized countries where populations are ageing will require priority policy measures. Ensuring that elderly people living in underserved areas have adequate access to primary care may prevent future hospitalizations, use of home care services and institutionalization.
Feasibility of displacement monitoring using low‐cost GPS receivers

Many of the available SHM approaches neither readily support displacement monitoring nor work in concert with one another to take advantage of displacement‐based SHM for various long‐period structures. Although survey‐quality GPS technology offers the possibility of measuring such displacements with sub‐centimeter precision, the associated cost is too high to allow for routine deployment. Low‐cost GPS chips commonly found in mobile phones and automobile navigation equipment are attractive in terms of size, cost, and power consumption; however, the displacement accuracy of these GPS chips is on the order of several meters, which is insufficient for SHM applications. Inspired by sensory information processing strategies of weakly electric fish, this paper investigates the potential for using dense arrays of relatively low‐precision GPS sensors to achieve high‐precision displacement estimates. Results show that dynamic response resolution as low as 20–30 cm can be achieved and that the resolution improves with the number of sensors used. Copyright © 2012 John Wiley & Sons, Ltd.
Training ESD change agents through geography: designing the curriculum of a master’s program with emphasis on Education for Sustainable Development (ESD)

Abstract To achieve a deep and wide implementation of Education for Sustainable Development (ESD) into all forms of education, the capacity of educators and trainers needs to be increased. This paper describes the curricular architecture of a master’s program in Geography with an emphasis on ESD. Based on a first feedback given by graduates, the paper discusses both strengths and weaknesses of the program.
Partitioning Trillion-Edge Graphs in Minutes

We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process trillion-edge graphs. XtraPuLP is based on the scalable label propagation community detection technique, which has been demonstrated as a viable means to produce high quality partitions with minimal computation time. On a collection of large sparse graphs, we show that XtraPuLP partitioning quality is comparable to state-of-the-art partitioning methods. We also demonstrate that XtraPuLP can produce partitions of real-world graphs with billion+ vertices in minutes. Further, we show that using XtraPuLP partitions for distributed-memory graph analytics leads to significant end-to-end execution time reduction.
Kinematic batch calibration for legged robots

This paper introduces a novel batch optimization based calibration framework for legged robots. Given a non-degenerate calibration dataset and considering the stochastic models of the sensors, the task is formulated as a maximum likelihood problem. In order to facilitate the derivation of consistent measurement equations, the trajectory of the robot and other auxiliary variables are included into the optimization problem. This formulation can be transformed into a nonlinear least squares problem which can be readily solved. Applied to our legged robot StarIETH, the framework estimates kinematic parameters (segment lengths, body dimensions, angular offsets), accelerometer and gyroscope biases, as well as full inter-sensor calibrations. The generic structure easily allows the inclusion of additional sensor modalities. Based on datasets obtained on the real robot the consistency and performance of the presented approach are successfully evaluated.
Biasing Attention-Based Recurrent Neural Networks Using External Alignment Information

This work explores extending attention-based neural models to include alignment information as input. We modify the attention component to have dependence on the current source position. The attention model is then used as a lexical model together with an additional alignment model to generate translation. The attention model is trained using external alignment information, and it is applied in decoding by performing beam search over the lexical and alignment hypotheses. The alignment model is used to score these alignment candidates. We demonstrate that the attention layer is capable of using the alignment information to improve over the baseline attention model that uses no such alignments. Our experiments are performed on two tasks: WMT 2016 English → Romanian and WMT 2017 German → English.
Serological Evaluation of Specific-Antibody Levels in Patients Treated for Chronic Chagas' Disease

ABSTRACT Serological tests are the main laboratory procedures used for diagnosis during the indeterminate and chronic stages of Chagas' disease. A serological regression to negativity is the main criterion used to define parasitological cure in treated patients. The aim of this work was to monitor the individual specificities of antibody levels for 3 years posttreatment in 18 adult patients. Conventional serological techniques (hemagglutination assays and enzyme-linked immunosorbent assay [ELISA]) were modified by using recombinant antigens to detect early markers of treatment effectiveness. For this purpose, serum samples were taken before and during treatment and every 6 months after treatment for at least 3 years. When hemagglutination assays were used, a decrease in antibody levels was observed in only one patient. When ELISA with serum dilutions was used, antibody clearance became much more apparent: in 77.7% (14/18) of the patients, antibody titers became negative with time. This was observed at serum dilutions of 1/320 and occurred between the 6th and the 30th months posttreatment. The immune response and the interval for a serological regression to negativity were different for each patient. For some of the recombinant antigens, only 50% (9/18) of the patients reached the serological regression to negativity. Recombinant antigen 13 might be a good marker of treatment effectiveness, since 66.6% (six of nine) of the patients presented with an early regression to negativity for specific antibodies to this antigen (P = 0.002).
Rapid Detection of Ofloxacin Resistance in Mycobacterium tuberculosis by Two Low-Cost Colorimetric Methods: Resazurin and Nitrate Reductase Assays

ABSTRACT We have evaluated the performance of two rapid, low-cost methods for the detection of ofloxacin (OFX) resistance with 95 Mycobacterium tuberculosis isolates from countries with high multidrug-resistant tuberculosis endemicity. Results obtained by nitrate reductase and resazurin assays showed 100% agreement with those of the proportion method on 7H11 agar using 2 μg of OFX/ml. We confirmed the resistance of all isolates found to be resistant to OFX by the Mycobacterium Growth Indicator Tube system, and complete agreement among all methods was observed. Nitrate reductase and resazurin assays are rapid, simple, low-cost methods and might become inexpensive alternative procedures for rapid detection of OFX resistance in low-resource countries.
Biomedical literature mining for pharmacokinetics numerical parameter collection

Model-based drug studies have been developing very fast recently. They require high quality pharmacokinetics (PK) parameter numerical data. However, most parameter measurements are still buried in the scientific literature. Traditional manual data extraction is too expensive to handle the exponentially growing number of publications. This thesis focuses on the application of text mining (TM) and machine learning (ML) for drug pharmacokinetics parameter data collection from the published literature. First, we explore the feasibility of TM on the extraction of drug PK parameter data from PubMed abstracts. Our method achieves higher precision and obtains rich information content. For the test drug Midazolam, it extracts 10 times more PK clearance data than the manually constructed commercial Drug Interaction Database (DiDB). Similar performance is obtained on additional test drugs. Following the success of TM on abstracts; we extended the methodology to full text articles and developed a literature mining pipeline for PK parameter data extraction, which is the first working approach to extract numerical data from full text articles, capable of processing both plain text and tabular data. The specific contributions of this thesis include: 1) A new PK ontology for entity template construction; 2) Comparison of NLP and machine learning algorithms for PK information retrieval; 3) Tabular data extraction; 4) PK information extraction from full text literature; 5) Multivariate nonlinear mixed model for PK parameter transformation.
A Mechanism for the Recently Increased Interdecadal Variability of the Silk Road Pattern

The Silk Road pattern (SRP) teleconnection manifests in summer over Eurasia, where it is associated with substantial temperature and precipitation anomalies. The SRP varies on interannual and decadal scales; reanalyses show an increase in its decadal variability around the mid-1970s. Understanding what drives this decadal variability is particularly important, because contemporary seasonal prediction models struggle to predict the phase of the SRP. Based on analysis of observations and multiple targeted numerical experiments, this study proposes a mechanism for decadal SRP variability. Causal effect network analysis confirms a positive feedback loop between the eastern portion of the SRP pattern and vertical motion over India on synoptic time scales. Anomalies over a larger region of subtropical South Asia can reinforce a background state that projects onto the positive or negative SRP through this mechanism. This effect is isolated and confirmed in targeted numerical simulations. The transition from weak to strong decadal variability in the mid-1970s is consistent with more spatially coherent interannual precipitation variability over subtropical South Asia. Furthermore, results suggest that oceanic variability does not directly force the SRP. Nevertheless, sea surface temperatures in the North Atlantic and the North Pacific may indirectly affect the SRP by modulating South Asian rainfall on decadal time scales.
Discovering Relations among Named Entities from Large Corpora

Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. Our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.